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Elorri Arevalo

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Etienne Prevost

Agnès Bardonnet

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Panserat Stéphane

Seiliez Iban

Larrañaga Aitor

Bardonnet Agnès

Comparison of the propensity to drift for three invertebrate taxa: a laboratory study

Keywords: invertebrates, modelling, water velocity, gravel bed quality, head capsule width, season Temperature, Salmo trutta, fry, starvation, prey, metabolism

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Abstract

Global Climate Change will increase precipitations in the temperate and Northern coast of Europe during winter and spring. In riverine ecosystems, precipitations affect strongly the discharge of running waters and, thus, it is predicted that streams will face more severe floods. Additionally, air and water temperature will increase all over the world. These new environmental conditions can alter the phenology of species and predator/prey interactions. Newborns of brown trout (Salmo trutta L.) start their exogenous feeding in March/April. This stage is a critical step as individuals undergo huge physiological and behavioral changes. To allow a good development and a high survival rate, preys have to be abundant, particularly during early ontogenesis when fish are most vulnerable to food scarcity and predation. In this thesis, experiments in controlled-environment were conducted to estimate the effect of water velocity on the drift of preferred prey taxa for salmonids and to understand the effect of temperature on the metabolism of alevins facing starvation. Experiments in semi-natural conditions were set up to better understand the effects of floods on invertebrate communities and on survival, behavior and growth of first-feeding alevins. Our data support that floods affect trout differently depending on when they start feeding (early or late spring) and the availability of prey in their environment.

Résumé

Le changement climatique devrait induire une augmentation des précipitations pendant l'hiver et le printemps dans les régions tempérées et la côte nord de l'Europe. Dans les écosystèmes fluviaux, les précipitations affectent fortement le débit des eaux courantes et les rivières subiront des crues plus sévères. En outre, la température de l'air et de l'eau augmenteront à travers le monde. Ces nouvelles conditions environnementales vont avoir des conséquences sur la phénologie des espèces et les interactions prédateurs/proies. Les jeunes truites fario (Salmo trutta L.) commencent leur alimentation exogène en mars/avril. Cette étape critique de leur cycle de vie induit d'importants changements aussi bien physiologiques que comportementaux. Pour permettre un bon développement des individus et un taux de survie élevé, les proies doivent être disponibles et abondantes, en particulier à ce moment de l'ontogénèse où les juvéniles sont vulnérables au manque de nourriture et à la prédation. Des expériences en milieux contrôlés ont été menées pour quantifier la sensibilité à la dérive de trois espèces d'invertébrés couramment consommées par les salmonidés en fonction de différentes modalités de vitesses de courant et pour évaluer l'effet de la température sur le métabolisme d'alevins en situation de jeûne. Des expériences en milieu semi-naturel ont été mises en place pour mieux comprendre les effets d'une crue sur la communauté d'invertébrés et sur la survie, le comportement et la croissance des alevins en première alimentation. Il apparaît que la crue impacte différemment les truites en fonction du moment de la saison auquel elles commencent à s'alimenter (au début ou à la fin du printemps) et de la productivité du système.

Resumen

Se espera que el cambio climático aumente las precipitaciones durante el invierno y la primavera en las regiones templadas y en la costa norte de Europa. En los ecosistemas fluviales, las precipitaciones influyen directamente al caudal y, por tanto, se espera que los ríos sufran inundaciones más severas.

Además, la temperatura del aire y del agua aumentarán en todo el mundo. Estas nuevas condiciones ambientales afectarán a la fenología de las especies y a las interacciones depredador-presa. Los individuos jóvenes de la trucha común (Salmo trutta L.) comienzan su alimentación exógena en marzo/abril. Esta etapa crítica en su ciclo de vida conlleva importantes cambios fisiológicos y de comportamiento. Con el fin de permitir un buen desarrollo de los individuos y una alta tasa de supervivencia las presas deben ser abundantes, especialmente durante las primeras etapas, que son más vulnerables a la escasez de alimentos y la depredación. En esta tesis se llevaron a cabo experimentos controlados para cuantificar la sensibilidad de tres especies de invertebrados comúnmente consumidos por los salmónidos a la velocidad del agua, y, para evaluar el efecto de la temperatura sobre el metabolismo de los alevines en ayuno. Además, con otros experimentos en ambientes semi-naturales se ha tratado de comprender mejor los efectos de las inundaciones en las comunidades de invertebrados y en la supervivencia, comportamiento y crecimiento de los alevines en el comienzo de la alimentación exógena. Parece que las inundaciones afectan a la trucha de forma diferente dependiendo de cuándo llegan a esta fase (a principios o finales de la primavera) y la disponibilidad de presas en su ambiente.

Laburpena

Klima-aldaketaren aurreikuspenen arabera eskualde epeletan eta Europako iparraldeko kostaldean prezipitazioak handitu egingo dira neguan eta udaberrian. Prezipitazioek zuzenean eragiten dute ibaien emarien igoera, eta hala, ibaiek uholde gogorragoak jasango dituztela aurreikusten da. Horrez gain, airearen eta uraren tenperaturak mundu osoan egingo du gora. Ingurumen baldintza berri hauek espezieen fenologia eta harrapari/harrapakinen arteko elkarrekintzetan aldaketak sortuko dituzte. Amuarrain arruntak (Salmo trutta L.) martxo/apirilean hasten dira elikadura exogenoa erakusten.

Bizitza-zikloaren etapa kritiko honek aldaketa garrantzitsuak dakartza fisiologian eta jokabidean. Aleen garapen egokia eta biziraupen-tasa handiak lortzeko, harrapakinak ugaria izan behar du, ontogeniaren lehen urratsetan batik bat, errekurtso eskasia eta predazioaren aurrean zaurgarrienak diren momentua bait da. Tesi honetan salmonidoek kontsumitzen dituzten hiru makroornogabe espezieek ur abiaduradi dioten sentsibilitatea estimatu zen. Bestalde, beste experimetu batean baraualdian zeuden alebinetan tenperaturak metabolismoan zuen eragina neurtu zen. Gainera, ornogabe komunitateetan eta elikatze exogenoan hasi berriak ziren alebinen biziraupenean, portaeran eta hazkundean uholdeek zuten eragina estimatu zen baldintza semi-naturaletan gauzatu ziren experimentuetan. Gure datuen arabera uholdeek eragin ezberdina dute amuarrainetan elikatzen hasten diren garaiaren arabera (udaberri hasieran edo bukaeran) eta inguruneak eskaintzen dien harapakin ugaritasunaren arabera.

General context

Growing in running waters

Streams are classified according to channel geomorphology (size, width, depth) and volume of water discharged [START_REF] Horton | Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology[END_REF][START_REF] Strahler | Statistical analysis in geomorphic research[END_REF][START_REF] Strahler | Quantitative analysis of watershed geomorphology[END_REF]. The smallest streams, namely first order streams, are located at the steepest parts of the watershed. The union of two first-order streams results in a second-order stream, and so on to sea; a sequence that entails important changes in functioning. Flowing waters have four distinct sources of energy (Allan & Castillo 2007;Giller & Malmqvist 1998). The first source comes from plants that use solar radiation via photosynthesis to produce instream (autochthonous) primary production. The other three sources of energy are imported into the running water system from the surrounding valley (allochthonous): coarse particulate organic matter (CPOMø > 1 mm), fine particulate organic matter (FPOM -0.5 µm < ø < 1 mm) and dissolved organic matter (DOMø < 0.5 µm) coming from the riparian vegetation or banks in the river. Then, autochthonous and allochthonous energy, in the form of organic matter, is transferred to consumers. The contribution of each source of energy varies along the river sections and changes the invertebrate community structure longitudinally, in a sequence that is known as the "River Continuum Concept" (RCC -Vannote et al. 1980). In headwater low order streams, most of the time dense canopies reduce the intensity of light and thus the amount of primary production. Then energy mainly comes from leaf inputs and invertebrate communities are principally composed by detritivores, mainly shredders. In mid-order streams, light conditions are improved and favour algal and macrophyte growth, stimulating the presence of grazers and scrapers. Finally, the scarce benthic light under the turbid waters of high order streams limit primary production again, and so, CPOM coming from the upstream parts are degraded in FPOM and consumed by the very abundant collectors.

Then, instream energy availability is closely related to the light availability and to the transport of materials, which regulate primary (instream or in riversides) and secondary production (i.e. biomass production of both invertebrates and fish).

The perpetuation of species involves the individuals to grow, complete their developmental cycle and reproduce. They choose their habitat to maximize access to food, shelter or partners.

Throughout the development their needs change and they may be forced to move from one habitat to another to optimise fitness. These habitat changes over the life of an organism are called "ontogenetic shifts" [START_REF] Werner | The ontogenetic niche and species interactions in sizestructured populations[END_REF]. In nature, growth and survival of fish depend on several factors. As they are ectotherms, water temperature governs many physiological processes such as respiration, excretion and growth. The seasonality, the abundance and the quality of food, within the constraints of the thermal regime, cause significant variability in fish production over time and space [START_REF] Benke | Bioenergetic considerations in the analysis of stream ecosystems[END_REF][START_REF] Huryn | An appraisal of the Allen paradox in a New Zealand trout stream[END_REF]Thompson & Beauchamp 2016) and appear as one of the main drivers that limit fish growth [START_REF] Wildhaber | Importance of the habitat choice behavior assumed when modeling the effects of food and temperature on fish populations[END_REF]. As they grow, mortality risks are reduced. The number of potential predators that can feed on fish diminishes [START_REF] Quinn | The influence of habitat complexity and fish size on over-winter survival and growth of individually marked juvenile coho salmon (Oncorhynchus kisutch) in Big Beef Creek, Washington[END_REF][START_REF] Sogard | Size-selective mortality in the juvenile stage of teleost fishes: a review[END_REF], and they also become better competitors and swimmers [START_REF] Young | Evolution of fighting behavior under asymmetric competition: an experimental test with juvenile salmonids[END_REF], which guarantee their access to food [START_REF] Ryer | Growth depensation and aggression in laboratory reared coho salmon: the effect of food distribution and ration size[END_REF], shelter [START_REF] Harwood | Intra-and interspecific competition for winter concealment habitat in juvenile salmonids[END_REF][START_REF] Orpwood | Effects of body size on sympatric shelter use in over-wintering juvenile salmonids[END_REF]. However, individuals have to face events, which can slow down their growth and threaten their survival.

The Match-Mismatch Hypothesis

During the developmental cycle of species, some stages are more vulnerable to food scarcity.

For example, the breeding period of the Atlantic puffin (Fratercula arctica) is synchronized with the peak of herring abundance, which are their main food items [START_REF] Durant | Trophic interactions under climate fluctuations: the Atlantic puffin as an example[END_REF][START_REF] Durant | Timing and abundance as key mechanisms affecting trophic interactions in variable environments[END_REF].

The synchronization between prey availability and predator requirements increase the survival rate of chicks, while a mismatch would produce a weaker cohort and population. [START_REF] Hjort | Fluctuations in the great fisheries of northern Europe viewed in the light of biological research[END_REF] firstly suggested that mortality of marine fishes (cod, haddock and Norwegian spring herring) was probably due to food scarcity during larval and young fry stages, which were the most critical periods in fish development. In addition, [START_REF] Cushing | The regularity of the spawning season of some fishes[END_REF] noticed that the spawning timing of marine fishes (herring, plaice and cod) allowed the hatching of the eggs and then, the development of larvae when the zooplankton production was high. Indeed, releasing larvae during the spring or autumn peaks in plankton production resulted in high survival rate for the three species of interest [START_REF] Cushing | Plankton production and year-class strength in fish populations: an update of the match/mismatch hypothesis[END_REF].

Following these observations on terrestrial and marine species, the "Match-Mismatch Hypothesis" (MMH) emerged and states that critical periods during which predators need energy occur simultaneously with the peak availability of prey. This way, recruitment of predators is maximised. On the contrary, the higher the mismatch between food requirements and food availability, the lower the growth, survival and recruitment of predators [START_REF] Durant | Climate and the match or mismatch between predator requirements and resource availability[END_REF][START_REF] Woodward | Climate change and freshwater ecosystems: impacts across multiple levels of organization[END_REF][START_REF] Bewick | How resource phenology affects consumer population dynamics[END_REF]. Mortality induced by prey scarcity is expected to be higher for fish larvae than for late stages because (i) larvae are not able to withstand prolonged fasting periods and (ii) smaller larvae are more susceptible to predation [START_REF] Dou | Effects of temperature and delayed initial feeding on the survival and growth of Japanese flounder larvae[END_REF][START_REF] Yokota | Effects of starvation at the first feeding stage on the survival and growth of walleye pollock Gadus chalcogrammus larvae[END_REF]. The mismatch between predator requirements and food availability can be due to the occurrence of disturbance, but, as far as we know, the MMH has never been tested in freshwater systems.

Global Climate Change threatening trophic links

In Europe, projections for 2100 from the Intergovernmental Panel on Climate Change (IPCC) and from several studies modelling the consequences of Global Climate Change (GCC) on the precipitation patterns, forecast an increase in the magnitude and frequency of extreme hydrological events [START_REF] Lehner | Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis[END_REF][START_REF] Dankers | Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations[END_REF]IPCC 2014) as well as a modification of events timing [START_REF] Blöschl | Changing climate shifts timing of European floods[END_REF]). In southern Europe, average seasonal precipitations is predicted to increase from 5 to 40% during winter (from December to February -Figure 1), whereas the decrease of precipitation will induce low flows the rest of the year. In central and northern Europe, precipitations are expected to drastically increase during winter but also in spring (from December to May). It can also be noted that the mean discharge of rivers are predicted to increase outside periods of extreme flood. On the contrary, the summer season (from June to August) will be more arid and droughts will be more extreme almost everywhere in Europe. Additionally, climate scenarios forecast an air temperature increase of 3°C, which will induce an elevation of the river water temperature between 0.8 and 1.6°C (van Vliet et al. 2013;Bal et al. 2014). [START_REF] Dankers | Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations[END_REF] In rivers, the increase of precipitations will increase the occurrence and the intensity of winter and spring floods. These new flow and temperature patterns can threaten the dynamic equilibrium of species. In particular, links between consumers and resources, which are often the result of a long co-evolution, might be weakened [START_REF] Woodward | Biodiversity, ecosystem functioning and food webs in fresh waters: assembling the jigsaw puzzle[END_REF][START_REF] Perkins | Global change and food webs in running waters[END_REF][START_REF] Woodward | Climate change and freshwater ecosystems: impacts across multiple levels of organization[END_REF]. Consequently, the increase in stochastic events due to GCC may threaten the synchronization of species phenology and peaks in prey abundance may be shifted (advanced or delayed). Predators may then face higher mortality rates due to starvation during critical periods.

Figure 1 Predictions from

Brown trout (Salmo trutta L.) as a case of study Repartition, ecological requirements and life cycle Brown trout is indigenous to Europe, North Africa and western Asia [START_REF] Klemetsen | Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories[END_REF].

It is present in many regions of Europe from north of Iceland, Scandinavia and Russia to South of the Mediterranean Sea. After many introductions, brown trout has now reached a world-wide distribution and is present in various biogeographic contexts (Elliott 1994). It is an important economical resource for professional and recreational fishing and it is frequently used as tourist attraction [START_REF] Aas | Angler responses to potential harvest regulations in a Norwegian sport fishery: a conjoint-based choice modeling approach[END_REF][START_REF] Butler | Evaluating an ecosystem service provided by Atlantic salmon, sea trout and other fish species in the River Spey, Scotland: the economic impact of recreational rod fisheries[END_REF].

At the river scale, brown trout prefer upstream sections of rivers [START_REF] Huet | Aperçu des relations entre la pente et les populations piscicoles des eaux courantes[END_REF][START_REF] Huet | Biologie, profils en long et en travers des eaux courantes[END_REF], with welloxygenated waters and neutral pH. Flow plays a major role in the distribution of individuals, supply of drifting invertebrates, redd oxygenation [START_REF] Baglinière | La truite. Biologie et écologie[END_REF]. They choose complex substrates that provide shelters from water velocity, predators and visual isolation between congeners [START_REF] Armstrong | Habitat requirements of Atlantic salmon and brown trout in rivers and streams[END_REF]. Lastly, brown trout is adapted to cool and quite narrow water temperature conditions (Elliott 1994).

Reproduction takes place from November to December, females dig a nest in the gravel, drop their eggs immediately fertilised by males and they cover the clutch with sediment. Eggs grow under the protection of gravel and they hatch around February, at 420 degree-days (i.e. the sum of the mean daily water temperature from the egg-laying). New-born alevins stay under the gravel, they feed on their yolk reserves. From March to April, when their yolk sac is almost exhausted (around 730 degree-days) alevins leave the protection of the redd and emerge in the water column to search for prey [START_REF] Roussel | The habitat of juvenile brown trout (Salmo trutta L.) in small streams: Preferences, movements, diel and seasonal variations[END_REF]. After spending at least one year in the river, juveniles can spend all their life in running waters ("riverine trout"), migrate to lake ("lake trout") or migrate to sea after physiological adaptations (smoltification; "sea trout" -Figure 2).

Figure 2 Life cycle of brown trout (Salmo trutta) according to life stages and to the different environments that the species is capable to use for growing (lake, freshwater and sea).

Emergence as a crucial step

The move from the under-gravel compartment to water column is called "emergence" and it is considered as a critical time for survival in brown trout with mortality rates as high as 90% [START_REF] Elliott | The critical-period concept for juvenile survival and its relevance for population regulation in young sea trout, Salmo trutta[END_REF]. Alevins undergo huge behavioural and physiological changes [START_REF] Einum | Selection against late emergence and small offspring in Atlantic salmon (Salmo salar)[END_REF]Skoglund & Barlaup 2006;Kennedy et al. 2008) and they must adapt quickly to a radically different environment. When they emerge, their yolk sac is almost exhausted and they shift to an exogenous feeding, which implies metabolic changes [START_REF] Mennigen | Ontogenetic expression of metabolic genes and microRNAs in rainbow trout alevins during the transition from the endogenous to the exogenous feeding period[END_REF]) and the establishment of the processes of digestion, absorption and assimilation [START_REF] Dabrowski | The feeding of fish larvae: present «state of the art» and perspectives[END_REF]. In addition to physiological changes, emergence involves behavioural modifications. Alevins need to maintain their position in the water column and catch prey that drift in water. Salmonids are territorial animals and a social hierarchy is established the week following emergence [START_REF] Héland | Social organization and territoriality in brown trout juveniles during ontogeny[END_REF]. Alevins compete with congeners (conspecifics from the same clutch and those from surrounding redds) to access to the best hunting spots. They are "sit-and-wait" hunters [START_REF] Elliott | The food of trout (Salmo trutta) in a Dartmoor stream[END_REF][START_REF] Cada | Evidence of food limitation of rainbow and brown trout in southern Appalachian soft-water streams[END_REF][START_REF] Giroux | Relationship between the drift of macroinvertebrates and the activity of brown trout in a small stream[END_REF]: they defend small territories (0.1-0.2 m² - Grant et al. 1998) close to shelters and near fast-flowing water that provide high quantity of prey. This strategy allows alevins to minimize their energetic expenditures while energy input is maximized by a large supply of food in the drift. These stations are energetically favourable [START_REF] Fausch | Profitable stream positions for salmonids: relating specific growth rate to net energy gain[END_REF] and are generally chosen by dominant individuals [START_REF] Jenkins | Social structure, position choice and micro-distribution of two trout species (Salmo trutta and Salmo gairdneri) resident in mountain streams[END_REF][START_REF] Fausch | Profitable stream positions for salmonids: relating specific growth rate to net energy gain[END_REF]Grant & Kramer 1990). The intensity of the competition depends greatly on the density of alevins. According to [START_REF] Elliott | The critical-period concept for juvenile survival and its relevance for population regulation in young sea trout, Salmo trutta[END_REF], density exerts a selective pressure during 20-30 days after emergence. Under high density, the scarcity of favourable habitat and/or feeding resource would diminish the growth and then survival of alevins (Figure 3 -Elliott 1989;[START_REF] Milner | The natural control of salmon and trout populations in streams[END_REF].

Figure 3 Relationship between parent stock (S eggs per 60 m 2 ) and number of survivors (R fish per 60 m 2 ) in May/June (black circles), August/September (empty triangles)modified from [START_REF] Elliott | The critical-period concept for juvenile survival and its relevance for population regulation in young sea trout, Salmo trutta[END_REF].

The timing of emergence influences density of alevins and depends on temperature, date of adult spawning and quantity of energy allocated by females to eggs [START_REF] Armstrong | Critical habitat during the transition from maternal provisioning in freshwater fish, with emphasis on Atlantic salmon (Salmo salar) and brown trout (Salmo trutta)[END_REF]Régnier et al. 2013). Early emergence give access to the best hunting spots but fish are more vulnerable to predation and to hydrological events such as large floods [START_REF] Einum | Selection against late emergence and small offspring in Atlantic salmon (Salmo salar)[END_REF]. Late emerging alevins mingle with early emerging survivors and alevin densities may be high. This can decrease the predation risk of alevins by piscivore fish due to a dilution effect [START_REF] Sogard | Size-selective mortality in the juvenile stage of teleost fishes: a review[END_REF][START_REF] Alvarez | Predator avoidance behaviour in wild and hatchery-reared brown trout: the role of experience and domestication[END_REF] but intensify competition [START_REF] Skoglund | Competitive interactions shape offspring performance in relation to seasonal timing of emergence in Atlantic salmon[END_REF].

Moreover, prey availability increases throughout spring, which should tend to favour alevins emerging later. First moments of salmonid life are crucial for growth and affect directly life history traits, strength of the cohort and population dynamics [START_REF] Bacon | Seasonal growth patterns of wild juvenile fish: partitioning variation among explanatory variables, based on individual growth trajectories of Atlantic salmon (Salmo salar) parr[END_REF][START_REF] Wysujack | The role of the environment in partial migration: food availability affects the adoption of a migratory tactic in brown trout Salmo trutta[END_REF][START_REF] Jonsson | Early environment influences later performance in fishes[END_REF]. Then, understanding key factors acting on growth and survival of alevins is of main importance to improve our understanding on brown trout population functioning (Elliott 1986).

Diet of Salmo trutta

In open waters, brown trout (alevins and juveniles) feed mainly on "macroinvertebrates", which are organisms living in/on the top layer of river bed (Tachet et al. 2010) and larger than 0.5 mm at their adult stage. Macroinvertebrates are of main importance in ecosystem processes: they contribute to the processing of allochthonous and autochthonous organic carbon, influence periphyton growth and represent the main feeding resource for many fish and birds living near rivers [START_REF] Quinn | Characterisation and classification of benthic invertebrate communities in 88 New Zealand rivers in relation to environmental factors[END_REF][START_REF] Wallace | The role of macroinvertebrates in stream ecosystem function[END_REF][START_REF] Malmqvist | Aquatic invertebrates in riverine landscapes[END_REF]. Stream macroinvertebrates spend their larval stage in freshwaters, while the adult stage takes place in terrestrial systems after a nymphal transformation but some species (such as Mollusca, Crustacea, Oligochaeta and some Coleoptera) spend their entire life cycle in rivers.

Macroinvertebrates colonize new habitats by drifting from upstream sources, crawling/swimming from adjacent substrates (including the hyporheic habitat) or adult flying [START_REF] Mackay | Colonization by lotic macroinvertebrates: a review of processes and patterns[END_REF]. Their instream distribution and abundance depend on a variety environmental factors [START_REF] Melo | Resistance, resilience, and patchiness of invertebrate assemblages in native tussock and pasture streams in New Zealand after a hydrological disturbance[END_REF]. At the watershed scale, altitude, local climate, topography, geology and catchment vegetation drive the assemblage of invertebrates [START_REF] Winterbourn | The use of aquatic invertebrates in studies of stream water quality[END_REF][START_REF] Li | Relationships between stream macroinvertebrates and environmental variables at multiple spatial scales[END_REF]. At the river scale, flow directly affects aquatic communities by influencing water quality, food sources, species interactions and the availability of diverse/heterogeneous habitats [START_REF] Jowett | Microhabitat preferences of benthic invertebrates in a New Zealand river and the development of in-stream flow-habitat models for Deleatidium spp[END_REF][START_REF] Quinn | Characterisation and classification of benthic invertebrate communities in 88 New Zealand rivers in relation to environmental factors[END_REF]. Water physico-chemistry (temperature, pH, dissolved oxygen concentration) and nutrient availability can also heavily impact the survival, reproduction and growth of invertebrates [START_REF] Stewart | Land use, habitat, and water quality effects on macroinvertebrate communities in three watersheds of a Lake Michigan associated marsh system[END_REF][START_REF] Rawi | Biological and ecological diversity of aquatic macroinvertebrates in response to hydrological and physicochemical parameters in tropical forest streams of Gunung Tebu, Malaysia: implications for ecohydrological assessment[END_REF].

Brown trout feed mainly on "drifting" aquatic larvae of invertebrates, which are carried by the current. However, brown trout exhibit flexible behaviour [START_REF] Allen | The Horokiwi Stream: a study of a trout population[END_REF][START_REF] Ringler | Individual and temporal variation in prey switching by brown trout, Salmo trutta[END_REF][START_REF] Waters | Fish production-benthos production relationships in trout streams[END_REF]) and can adapt their hunting mode to maximize their energetic gain (Optimal Foraging Theory -OFT; [START_REF] Pyke | Optimal foraging: a selective review of theory and tests[END_REF]. They can search prey in the benthos (Ware 1972) or they can pick up terrestrial invertebrates at the water surface [START_REF] Dahl | Impact on stream benthic prey by benthic vs drift feeding predators: a meta-analysis[END_REF][START_REF] Huryn | An appraisal of the Allen paradox in a New Zealand trout stream[END_REF][START_REF] Nakano | Selective foraging on terrestrial invertebrates by rainbow trout in a forested headwater stream in northern Japan[END_REF]. The main factor driving trout prey selection is prey size. Salmonids are gap-limited predators (Sánchez-Hernández et al. 2011) and their access to food increases as the size of their mouth and oesophagus increases. Gut content analyses of newly emerged alevins revealed that prey were selected up to 0.50 mm width even if prey up to 2 mm width could be consumed [START_REF] Bozek | Diet overlap among size classes of Colorado River cutthroat trout (Oncorhynchus clarki pleuriticus) in a high-elevation mountain stream[END_REF]Domagała et al. 2014) and between 2 and 5 mm length (Wankowski 1989;[START_REF] Hubert | Sizes of prey consumed by age-0 brown trout in Douglas Creek, Wyoming[END_REF][START_REF] Keeley | Prey size of salmonid fishes in streams, lakes, and oceans[END_REF]. Another important factor in prey selection is the morphological and behavioural features of invertebrates [START_REF] Poff | Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships[END_REF]. According to these traits, the probability to be captured by a fish differs among taxa. [START_REF] Rader | A functional classification of the drift: traits that influence invertebrate availability to salmonids[END_REF] broadly reviewed traits affecting the availability of invertebrates for drift-feeding fish and all traits related to the propensity of invertebrates to drift are of main importance.

Invertebrates intentionally drifting or those easily dislodged by the water current are more likely to be preyed. Moreover, flow exposure depends on the use of the habitat and shelters [START_REF] Negishi | An experimental test of the effects of food resources and hydraulic refuge on patch colonization by stream macroinvertebrates during spates[END_REF][START_REF] Fuller | Response of stream macroinvertebrates in flow refugia and high-scour areas to a series of floods: a reciprocal replacement study[END_REF]): invertebrates inhabiting the hyporheic zone are less susceptible to be carried away by the current than invertebrates living at the surface of stones (Ware 1972). The mobility (i.e. sessile, attached, crawling or swimming) and the shape (i.e. streamlined, cylindrical, dorsoventrally-flattened or spherical) of invertebrates also influence their probability to be washed by the flow as the drag force exerted by the water on individuals depends on their length, width and height (Naman et al. 2016;[START_REF] Schülting | Effects of hydro-and thermopeaking on benthic macroinvertebrate drift[END_REF]. However, the critical factor remains the abundance of the taxon in the system. An abundant taxon, prone to drift, is highly available for fish (Crespin de Billy & Usseglio-Polatera 2002).

Considering the catchability, drift propensity, abundance, energetic profitability of prey and size limitation of salmonid predators, [START_REF] Rader | A functional classification of the drift: traits that influence invertebrate availability to salmonids[END_REF] ranked first the Baetidae family (Order: Ephemeroptera), second the Simuliidae family (Order: Diptera) and third the Chironomidae family (Order: Diptera). Next, the most available prey were from the Ephemeroptera order (i.e. Heptageneidae, Ephemerellidae, Leptophlebiidae and Siphonuridae), and Crustacea (Amphipoda). Many researchers confirmed these findings and showed that Baetidae, Simuliidae and Chironomidae larvae were the most important food items in gut contents of brown trout in different geographical areas [START_REF] Mccormack | The food of young trout (Salmo trutta) in two different becks[END_REF][START_REF] Elliott | The food of trout (Salmo trutta) in a Dartmoor stream[END_REF][START_REF] Fahy | Prey selection by young trout fry (Salmo trutta)[END_REF]Vignes & Heland 1995;Sánchez-Hernández et al. 2011 -Figure 4) and provided over 80% of the energetic inputs of new-borns alevins [START_REF] Sánchez-Hernández | Ontogenetic dietary shifts in a predatory freshwater fish species: the brown trout as an example of a dynamic fish species[END_REF].

Figure 4 Invertebrates most available and consumed by drift-feeding fish, including brown trout alevins. From left to right: Baetidae, Simuliidae and Chironomidae families. Photo from: http://lifeinfreshwater.net.

High discharge and the biota

The increase of discharge rises the scouring force of water on the bottom, called shear stress, and displaces the organisms downstream, which in turn alters the composition of instream communities [START_REF] Leigh | Ecological effects of extreme climatic events on riverine ecosystems: insights from Australia[END_REF]. The tight link between the discharge regime and macroinvertebrates has been described by many researchers. The optimal range of water velocities for invertebrates varies between 0.1 m s -1 and 0.6 m s -1 . Flow velocities higher than 0.7 m s -1 are not considered suitable for most macroinvertebrates [START_REF] Gore | Macroinvertebrate instream flow studies after 20 years: a role in stream management and restoration[END_REF][START_REF] Li | Construction of habitat suitability models (HSMs) for benthic macroinvertebrate and their applications to instream environmental flows: a case study in Xiangxi River of Three Gorges Reservior region, China[END_REF][START_REF] Horta | Assessment of benthic macroinvertebrate habitat suitability in a tropical watershed[END_REF][START_REF] Shearer | Habitat suitability curves for benthic macroinvertebrates from a small New Zealand river[END_REF].

In rivers, "hydropeaking" refers to frequent discharges pulses generated by hydroelectric power generation [START_REF] Bratrich | Green hydropower: a new assessment procedure for river management[END_REF][START_REF] Bretschko | Downstream effects of intermittent power generation[END_REF][START_REF] Bruno | Multiple drift responses of benthic invertebrates to interacting hydropeaking and thermopeaking waves[END_REF]) and several studies showed that hydropeaking causes an increase in the number of macroinvertebrate drifting and a reduction of macroinvertebrate biomass and abundance in the benthos [START_REF] Moog | Quantification of daily peak hydropower effects on aquatic fauna and management to minimize environmental impacts[END_REF][START_REF] Céréghino | Influence of hypolimnetic hydropeaking on the distribution and population dynamics of Ephemeroptera in a mountain stream[END_REF][START_REF] Céréghino | Influence of intermittent hydropeaking on the longitudinal zonation patterns of benthic invertebrates in a mountain stream[END_REF][START_REF] Bruno | Multiple drift responses of benthic invertebrates to interacting hydropeaking and thermopeaking waves[END_REF][START_REF] Miller | Responses of macroinvertebrate drift, benthic assemblages, and trout foraging to hydropeaking[END_REF].

Similarly, natural extremely large floods can reduce invertebrate abundances between 15 and 90%, while invertebrate diversity is reduced by 70% or not at all affected [START_REF] Melo | Resistance, resilience, and patchiness of invertebrate assemblages in native tussock and pasture streams in New Zealand after a hydrological disturbance[END_REF][START_REF] Argerich | Effect of floods of different magnitude on the macroinvertebrate communities of Matarranya stream (Ebro river basin, NE Spain)[END_REF][START_REF] Mesa | Effect of spates and land use on macroinvertebrate community in Neotropical Andean streams[END_REF]. McMullen & Lytle (2012) conducted a meta-analysis for the link between flood events and changes in invertebrate communities with 41 studies spread across the world. Despite the differences in river type and regional climate, all these studies concluded that the total abundance of invertebrates as well as the abundance of the major groups of invertebrates significantly decrease immediately after floods. Moderate flow events have also been responsible for the reduction of invertebrate abundance and diversity by 90% and 25%, respectively [START_REF] Theodoropoulos | Response of freshwater macroinvertebrates to rainfall-induced high flows: A hydroecological approach[END_REF].

In brown trout, critical water velocities beyond which fish cannot maintain their position are 0.25 m s -1 for recently emerged alevins (body length around 3 cm - [START_REF] Heggenes | Downstream migration and critical water velocities in stream channels for fry of four salmonid species[END_REF]Bardonnet & Héland 1994) and 0.7 m s -1 for adults (around 21 cm -Heggenes 1988). High water velocities can alter recruitment (i.e. the juveniles incorporating the population) by destroying redds, displacing individuals [START_REF] Seegrist | Effects of floods on trout in Sagehen Creek, California[END_REF][START_REF] Wenger | Role of climate and invasive species in structuring trout distributions in the interior Columbia River Basin, USA[END_REF] or diminishing the abundance of suitable microhabitats for feeding and sheltering [START_REF] Lobón-Cerviá | Population size in stream-living juveniles of lakemigratory brown trout Salmo trutta L.: the importance of stream discharge and temperature[END_REF]. Temporal variations in water velocity are known to limit survival and growth rates of the youngest juveniles [START_REF] Lagarrigue | Diel and seasonal variations in brown trout (Salmo trutta) feeding patterns and relationship with invertebrate drift under natural and hydropeaking conditions in a mountain stream[END_REF][START_REF] Korman | Effects of fluctuating flows and a controlled flood on incubation success and early survival rates and growth of age-0 rainbow trout in a large regulated river[END_REF][START_REF] Lobón-Cerviá | Discharge-dependent covariation patterns in the population dynamics of brown trout (Salmo trutta) within a Cantabrian river drainage[END_REF][START_REF] Lobón-Cerviá | Spatial and temporal variation in the influence of density dependence on growth of stream-living brown trout (Salmo trutta)[END_REF] highlighted the importance of river discharge at the time of emergence and its influence on the population structure over the years. Indeed, year-to-year variation in river discharge at the time of emergence consistently matched recruitment rates, survival rates of mean cohort size and abundance of spawners. This phenomenon has been described for a variety of populations across the European range of brown trout [START_REF] Elliott | Variable effects of droughts on the density of a sea-trout Salmo trutta population over 30 years[END_REF][START_REF] Jensen | The functional relationship between peak spring floods and survival and growth of juvenile Atlantic salmon (Salmo salar) and brown trout (Salmo trutta)[END_REF][START_REF] Mäki-Petäys | Densities of juvenile brown trout (Salmo trutta) in two subarctic rivers: assessing the predictive capability of habitat preference indices[END_REF][START_REF] Cattanéo | The influence of hydrological and biotic processes on brown trout (Salmo trutta) population dynamics[END_REF][START_REF] Cattanéo | Synchrony in brown trout, Salmo trutta, population dynamics: a 'Moran effect'on early-life stages[END_REF]. Similarly, numerous introduced populations across geographical (climatic) North American regions appear to be constrained by discharge variations (Strange et al. 1992;[START_REF] Nehring | Determination of population-limiting critical salmonid habitats in Colorado streams using the Physical Habitat Simulation System[END_REF][START_REF] Nuhfer | Recruitment of brown trout in the south branch of the Au Sable River, Michigan in relation to stream flow and winter severity[END_REF][START_REF] Latterell | Relationship of trout recruitment to snowmelt runoff flows and adult trout abundance in six Colorado mountain streams[END_REF]. [START_REF] Elwood | Effects of floods on food consumption and production rates of a stream brook trout population[END_REF] suggested that declines in invertebrate populations caused by floods could reduce the food availability for fish causing an apparent decrease in their growth. However, it is difficult to disentangle the relative importance of these factors (i.e. diminution in habitat or trophic availability) in the mortality of alevins.

Synthesis and main objectives of this work

In brown trout, alevins emerge in spring, which currently matches with the increase in abundance of invertebrates in the drift [START_REF] O'hop | Invertebrate drift, discharge, and sediment relations in a southern Appalachian headwater stream[END_REF]Romaniszyn et al. 2007;[START_REF] Leung | Habitat effects on invertebrate drift in a small trout stream: implications for prey availability to drift-feeding fish[END_REF]) after the usually low abundances in temperate areas in winter (Brittain & Eikeland 1988). Besides, literature results seem to underscore a negative effect of hydrological events on both fish survival and growth. Because growth is highly density-dependant in salmonids [START_REF] Jenkins | Effects of population density on individual growth of brown trout in streams[END_REF][START_REF] Vøllestad | Growth-rate variation in brown trout in small neighbouring streams: evidence for density-dependence[END_REF] one can suppose that the mechanism acting on 0 + cohort following spates is related to low food availability (the Match-Mismatch Hypothesis), and/or suitable habitat availability. In this thesis, we carried out experiments which should help assessing to what extend Global Climate Change and more specifically the awaited increase in both spring temperature and precipitations might affect the post-emergence survival and growth of trout in relation to food shortage. This manuscript is composed of four chapters. The first chapter, based on an experimental study, investigates the drift propensity of three invertebrate taxa (Baetis, Simulium and Chironomus), which form the bulk of the diet of young salmonids. The second chapter, also conducted in a controlled environment, examines the metabolic pathways of just-emerged alevins facing starvation or a delayed first-feeding at 8 and 11°C. The third chapter relies on an experiment conducted in a semi-natural environment and tries to shed light on the consequences of an artificial flood on the invertebrates and on the survival, growth and behaviour of young trout at high density. The fourth chapter describes the consequences of a simulated flood on the invertebrate community and on the survival, growth and diet of trout alevins at low density. The last part of this manuscript draws a general discussion and proposes some hypotheses and future research pathways about the effect of Global Climate Change on young stages of salmonids.

Chapter I Drift of invertebrates: effects of taxa, water velocity, gravel bed quality and body size

Study context

In research areas focusing on drift-feeding fish, their growth is predicted from foraging and bioenergetic models. The Net Rate of Energy Intake of fish (NREI) is usually based on temperature and food availability [START_REF] Hayes | Development and test of a whole-lifetime foraging and bioenergetics growth model for drift-feeding brown trout[END_REF][START_REF] Laliberte | Modelling Temperature, Body Size, Prey Density, and Stream Gradient Impacts on Longitudinal Patterns of Potential Production of Drift-Feeding Trout[END_REF]. Food availability is directly related to the density of drifting invertebrates and to water velocity. The increase in water velocity increases the invertebrate probability of being dislodged from the benthos and then, the number of drifting invertebrates [START_REF] Brooker | The effect of the release of an artificial discharge of water on invertebrate drift in the R. Wye, Wales[END_REF]. However, Hayes et al. (2007) and [START_REF] Leung | Habitat effects on invertebrate drift in a small trout stream: implications for prey availability to drift-feeding fish[END_REF] emphasized that no studies provided an accurate estimation of the entry rate of invertebrates in the drift, which conditions the pertinence of the models.

Moreover, [START_REF] Elliott | The distances travelled by drifting invertebrates in a Lake District stream[END_REF] investigated the distance travelled by invertebrates releasing them in running waters and he highlighted that an increase in water velocity carried the invertebrates further. Although this information is valuable for improving fish bioenergetic models, very few drift studies have attempted to predict invertebrate entry rate. To better understand the effect of floods on the food availability of salmonids, it is necessary to begin by understanding the response of invertebrates and their propensity to drift when facing water at various velocities.

Furthermore, the nature of the substrate is of prime importance. Stability of the gravel bed refers to its resistance to displacement and is generally proportional to particle size (Giller & Malmqvist 1998). Then, large particles increase the stability of the gravel bed, provide safe microenvironments and diminish accidental drift due to the dislodgment of invertebrates [START_REF] Cobb | Effects of discharge and substrate stability on density and species composition of stream insects[END_REF]. Number of shelters available for invertebrates depends on the number of interstices in the gravel bed, their size and the size of invertebrates [START_REF] St Pierre | Effect of habitat complexity attributes on species richness[END_REF].

In this experiment, we aimed to evaluate the drift propensity of three invertebrate taxa chosen because of their significant contribution to the diet of salmonid alevins: Baetis sp., Simulium sp. and Chironomus sp.. The variation of the drifting propensity was estimated in six indoor channels according to two environmental factors (water velocity and gravel bed quality) and a biotic factor (size of individuals).

Introduction

Invertebrate drift is defined as the downstream displacement of organisms and it is an important ecological process in lotic systems [START_REF] Waters | Interpretation of invertebrate drift in streams[END_REF]Brittain & Eikeland, 1988). Drift contributes to the dispersal of invertebrate species across the water network, allows connection between habitats and facilitates recolonization processes. [START_REF] James | The effect of experimental flow reductions on macroinvertebrate drift in natural and streamside channels[END_REF] differentiate two drift categories: passive drift with animals unintentionally entering the water column and active drift with animals intentionally entering the water column.

Drift rate highly depends on abiotic (i.e. water chemistry - [START_REF] Hay | Macroinvertebrate drift density in relation to abiotic factors in the Missouri River[END_REF], quantity of food -Ciborowski, 1983) and biotic (i.e. density of organisms - [START_REF] Lehmkuhl | Microdistribution and density as factors affecting the downsteam drift of mayflies[END_REF][START_REF] Walton | The effects of density, sediment particle size and velocity on drift of Acroneuria abnormis (Plecoptera)[END_REF], predation avoidance -Skinner, 1985;[START_REF] Sagar | Diel changes in the abundance and size composition of invertebrate drift in five rivers in South Island, New Zealand[END_REF] factors and to determine the effect of each is extremely challenging. In addition, taxa respond differently according to their morphology, behaviour and ecology [START_REF] Poff | Drift response of benthic invertebrates to experimental streamflow variation in a hydrologically stable stream[END_REF].

Many studies have attempted to investigate the effect of water velocity on invertebrate drift rate. The force exerted by the water on the river bed is called "shear stress" (Giller & Malmqvist, 1998;[START_REF] Vericat | Sediment entrainment and depletion from patches of fine material in a gravel-bed river[END_REF] and erodes mineral and biotic particles. The increase in water velocity within the channel increases the shear stress which then increases the risk of dislodgment and the passive transport of organisms downstream. [START_REF] Gibbins | When is stream invertebrate drift catastrophic? The role of hydraulics and sediment transport in initiating drift during flood events[END_REF] observed the active drift of invertebrates up to a shear stress of 9 dynes cm -2 (1 dyne cm -2 = 1 Newton m - 2 ) and above this threshold, drift was mainly passive due to the displacement of the gravel. All sheltered invertebrates are inevitably carried away by the current when the gravel bed is scoured. High shear stress mobilises substrate and fine particles and results in a patchwork of both scoured and infilled stream beds [START_REF] Carling | Bed stability in gravel streams, with reference to stream regulation and ecology[END_REF][START_REF] Lake | Disturbance, patchiness, and diversity in streams[END_REF][START_REF] Matthaei | Long-term effects of local disturbance history on mobile stream invertebrates[END_REF].

High water velocities recorded during extreme flow events increase passive drift and the number of drifting invertebrates [START_REF] Bruno | Responses of benthic invertebrates to repeated hydropeaking in semi-natural flume simulations[END_REF][START_REF] Radford | A preliminary investigation of bottom fauna and invertebrate drift in an unregulated and a regulated stream in Alberta[END_REF], with reductions in the abundance of benthic invertebrates ranging from 14 to 95% depending on the magnitude of the event [START_REF] Matthaei | Response of benthic invertebrates to natural versus experimental disturbance in a Swiss prealpine river[END_REF]Nislow et al., 2002;Robinson et al., 2004).

However, [START_REF] Theodoropoulos | Response of freshwater macroinvertebrates to rainfall-induced high flows: A hydroecological approach[END_REF] highlighted the lack of studies that examined the response of freshwater communities to medium water velocities (i.e. outside flood periods) and Hayes et al. (2007) go further by pointing out that the entry rate of invertebrates in the drift has yet to be quantified.

Recently, Naman et al. (2016) investigated the response of invertebrates to flow disturbance (from 8 cm s -1 to 28 cm s -1 ) according to channel architecture (flat vs concave channels) and taxa. The drifting community was comprised of a caddisfly (Micrasema sp.) and several taxa of Chironomidae, which for the most part, drifted passively. For the other drifting taxa, drift was primarily active. The shear stress exerted by the water was equal to 5 dynes cm -2 and not enough to dislodge invertebrates.

The quality of the gravel bed, especially the size of the particles, plays an important role by providing invertebrates with hydraulic refuges to avoid being dislodged. Results from field studies failed to separate the effect of water velocity, depth and substratum quality on passive drift of invertebrates because to date, assessments have been done at the river scale [START_REF] Lancaster | Small-scale movements of lotic macroinvertebrates with variations in flow[END_REF][START_REF] Lancaster | Geometric scaling of microhabitat patches and their efficacy as refugia during disturbance[END_REF]. Most data stemming from studies that examined the role of specific habitat parameters on invertebrate drift were drawn from experiments under controlled conditions and showed a higher drift rate in small gravel compared to cobble for caddisflies, mayflies [START_REF] Holomuzki | Sediment texture mediates high-flow effects on lotic macroinvertebrates[END_REF] and stoneflies [START_REF] Long | The effects of water velocity and sediment size on Acroneuria abnormis (Plecoptera: Perlidae) entrainment[END_REF] when submitted to spates.

According to these results, the size of the interstices, linked to invertebrate size, could play an important role in the availability of hydraulic refugia.

In the present work we focus on the drift of three invertebrate taxa that have different habitat preferences and locomotor behaviour: Baetis sp., Simulium sp. and Chironomus sp.. They are abundant in riverine ecosystems and they contribute significantly to the diet of drift-feeding fish (Sánchez-Hernández et al., 2011). In experimental channels we manipulated the size of the benthic instertices using different sized substrates. We also considered the size of invertebrates.

The interstice size-body interaction has rarely been related to drift propensity at the intraspecific level. Thus, the present study aims to quantify the propensity to drift according to taxa, water velocity, gravel bed quality and invertebrate size. We specifically tested the following hypotheses: 1) free swimming Baetis should actively drift more than Simulium (attached to the substrate with a suction cup) and Chironomus (buried under the substrate), 2) an increase in the water velocity should induce passive drift and increase the drift probability of invertebrates, 3) gravel size should mitigate passive drift with large particles diminishing drift probability of invertebrates, 4) large individuals should passively drift more than small ones due to scarcer shelter opportunities.

Material and Methods

Invertebrate collection

We collected invertebrates from two tributaries of the Nivelle River near Saint-Pée-sur-Nivelle (43°21' N, 1°33' W), on the 9 th and the 10 th of March and on the 19 th and the 20 th of October 2015. Baetids (Baetis sp.) were sampled from the Lapitxuri Brook, a pristine headwater stream.

Simuliids (Simulium sp.) were sampled downstream from the spillway of a lake, in a fastflowing brook characterised by a high percentage of fine sediment. Chironomids (Chironomus sp.) were purchased from Grebil (Arry, France). After collection, all invertebrates were kept in containers with aerated water at a constant temperature (ca. 13 ºC). Rocks with biofilm and conditioned leaves collected in the field were added to give them food and refuge until the start of the experiment, a few days later.

Experimental features

The experiments were conducted in 6 indoor channels (L: 150 cm, W: 10 cm and H: 12 cm) that were continuously fed in parallel by water pumped from a large outdoor reservoir (3000 m 3 ) filled with Nivelle River water. The water from the reservoir passed through a sand filter in order to remove most invertebrates, though we still recovered some small cyclops, oligochaetes and chironomids during the experiment. At this point, the only risk of confusion between the "experimental invertebrates" and the "natural invertebrates" concerned chironomids, but the larger size and the red colour of the reared taxon (Chironomus sp.) made it easy to separate wild from experimental animals. After the sand filter, water was sent to a 25 m 3 reservoir and pumped to a small reservoir (0.5 m 3 ) that fed by gravity the six experimental channels. The water level in the small reservoir was kept constant through an overflow. Water temperature followed natural changes, since water came from the outdoor reservoir and was registered every 15 min using a temperature logger (mini logger IIT, Vemco). Mean water temperature was 12.2ºC (± 0.37, SD) in March and 14.7ºC (± 0.50) in October. Light was controlled to mimic natural day and night rhythms. The lit period was chosen to match the natural photoperiod, and 30 min of progressive change in light levels were programmed to produce a 30 min period of dusk at 19:00 in March and 19:30 in October, and dawn at 6:30 in March and 7:30 in October. This results in quite similar L:D durations (12:11 in March and 11.5:11.5 in October).

Two types of gravel beds were created: a "fine" gravel bed (2-4 mm: 33%; 4-8 mm: 34%; 8-16 mm: 33%) and a "coarse" gravel bed (2-4 mm: 25%; 8-16mm: 50%; 20-31.5mm: 25%).

Channels were filled to a depth of 3 cm for one gravel type, resulting in 3 fine and 3 coarse channels. Velocities were measured with a mini-velocimeter laid directly on the substratum (Schiltknecht, MiniWater®20 -Table 1, measure depth: 11 mm above the substratum). Three different flow levels were applied to both fine and coarse gravel channels: "low flow" (0.2 l s -1 ~ 14.3 cm s -1 ), "medium flow" (4 times increase, 0.8 l s -1 ~ 29.8 cm s -1 ), "high flow" (8 times increase, 1.6 l s -1 ~ 39.6 cm s -1 ).

Experiments began at 9:00 and lasted for 27 hours. Between 9:00 and 10:00, 50 individuals from each taxon were added to each channel (no flow). A stepwise increase in non-scouring flow was carried out for one hour to avoid an abrupt and high amount of drift [START_REF] Imbert | Drift and benthic invertebrate responses to stepwise and abrupt increases in non-scouring flow[END_REF]. At 10:00, flow was opened at the low flow level for all channels, and remained at this level for two hours. From 12:00 to 13:00, the flow was increased every 15 min to mimic a gradual increase in water velocity, up to the high flow in channels 1 and 2, and up to medium flow in channels 3 and 4 (Table 1). Channels 5 and 6 were kept at low flow. From 10:00 onward, total hourly drift of invertebrates was gathered at the output of each channel in buckets equipped with two 10 x 10 cm openings closed by a 100 µm mesh net.

During the period of increasing flow (12:15, 12:30 and 12:45) and for each non-sampled twilight time-frame (19:30 and 6:30 in March, and 19:30 and 7:30 in October), additional samples were taken. The last sample was taken on the second day at 12:00. A total of 33 drift samples were collected per channel and, at the end of the experiment, all invertebrates still present in the channels were recovered manually from the substratum (Table S1). Water velocity was also measured at this time, just prior to the recovery of the animals, longitudinally along each channel at 20 cm intervals.

All animals were stored in 70% ethanol and photographed under a binocular microscope (Olympus SZX16). Both Head Capsule Widths (HCW) and Body Lengths (BLfrom the top of the head to the end of the abdomen) were measured with the software ImageJ (Abràmoff et al., 2004).

Statistical analyses

We did not account for missing individuals (equivalent to 12% of the individuals, including those not in the drift and those not recovered at the end). From the 1589 individuals that were collected and measured, we discarded animals collected before the beginning of the flow increase, i.e. during the settling period. Thus, 1396 individuals were used in the analyses and we assumed that the propensity/decision to drift of an individual was independent from that of the others. Raw data are shown in Figure 1. Data were then analysed using a modelling approach computed with OpenBUGS®. For each of the j taxon, if p.Di is the probability of drifting of the i th individual, we assume: Hill, 2006). Models were tested (Table 2) and the model with the lowest deviance information criterion (DIC) was selected [START_REF] Spiegelhalter | Bayesian measures of model complexity and fit[END_REF].

𝐷 𝑖 ~
We used three independent chains, the first 10 000 iterations were discarded as an initial burnin period. Then, 10 000 further iterations (resulting from one every ten runs) were performed.

The convergence of the chains to their ergodic distribution was tested via the Gelman-Rubin (GR) diagnostics integrated in OpenBUGS®.

The significance of the parameters was tested with the step function implemented in OpenBUGS®. At each iteration for a variable X, step(X) equaled 1 if X ≥ 0 and equaled 0 if X < 0. At the end of the run, if P(X > 0) was lower than 0.1 or higher than 0.9, the parameter X was considered to be different from 0.

Results

The best model with the lowest DIC included the effect of water velocity (α), HCW (γinstead of BL) and season (η), while the habitat (β) was excluded (DIC = 1573 -Table 2). Under our conditions, the gravel bed did not affect the drift probability for any taxa. When submitted to low, medium and high water velocities, a taxa-specific drifting behaviour was observed (Figure 2). Although all three taxa followed the same pattern and increased their drift probability with water velocity, only Chironomus showed a significant relationship (P(αC > 0) = 0.997 ; P(αB > 0) = 0.889 and P(αS > 0) = 0.841 -Table 3). At low water velocity (10 cm s -1 ), 44% of Baetis, 16% of Simulium and 26% of Chironomus drifted. At medium water velocity (30 cm s -1 ), 53% of Baetis, 21% of Simulium and 45% of Chironomus drifted. At high velocity (40 cm s -1 ), 58% of Baetis, 25% of Simulium and 55% of Chironomus drifted. Head capsule width was not related to the propensity to drift in Baetis (P(γB > 0) = 0.609).

Simulium showed a significant relationship between HCW and drift (P(γS > 0) = 0.000), similar to Chironomus (P(γC > 0) = 0.012), with small individuals drifting more than large ones in both cases (Figure 3). The effect of season on the drifting rates (η) was significant for Baetis and Simulium (P(ηB > 0) = 0.999; P(ηC > 0) = 0.200 and P(ηS > 0) = 0.001). The drift probability of Baetis was higher in October than in March (0.522 in March and 0.782 in October -Figure 4) while the drift probability of Simulium was lower (0.206 in March and 0.073 in October). No pattern appeared in the estimations of δ and few values were different from 0 (Table S2), which indicates that no interactions were detected.

Discussion

This experimental study provides a quantification of the drift rate for three taxa of invertebrates selected because of their abundance in riverine ecosystems and their potential contribution to drift-feeding fish (Sánchez-Hernández et al., 2011). The drift of Chironomus was positively related to water velocity while a positive, but non-significant, trend was observed for Baetis and Simulium. There was no effect of the substrate size (fine/coarse gravel bed) on any taxa and small Simulium and Chironomus drifted more than large ones. A seasonal effect was also detected, with Baetis drifting more in fall and Simulium drifting more in spring.

Under normal flow conditions, invertebrates are distributed along the river depending on their habitat preferences, their ability to cope with water velocity and their trophic requirements [START_REF] Fjellheim | Distribution of benthic invertebrates in relation to stream flow characteristics in a Norwegian river[END_REF][START_REF] Rempel | Macroinvertebrate community structure along gradients of hydraulic and sedimentary conditions in a large gravel-bed river[END_REF]. Baetis is a rheophilic taxon and is commonly associated with medium flow habitat, between 25 and 50 cm s -1 (Tachet et al., 2010). They prefer stones that are exposed to water current, as this exposure allows them to feed on benthic microscopic algae and to benefit from the high dissolved oxygen levels of swift currents. Their location above the gravel bed and exposure to the current make them very prone to drift [START_REF] Kohler | Positioning on substrates, positioning changes, and diel drift periodicities in mayflies[END_REF][START_REF] Kohler | Identification of stream drift mechanisms: an experimental and observational approach[END_REF]. Accordingly, Baetis demonstrated the highest drift propensity among the three taxa tested. [START_REF] Gibbins | The relationship between sediment mobilisation and the entry of Baetis mayflies into the water column in a laboratory flume[END_REF] highlighted that 50% of Baetis mayflies drifted with a fixed-bed sediment and that drift rate increased only when water current was strong enough to initiate bed-load transport. In the present experiment, flow was not high enough to mobilise the substrate to induce the passive drift of Baetis. As such, and in accordance with literature [START_REF] Fonseca | Density-dependent dispersal of black fly neonates is mediated by flow[END_REF], we assume that in the present experiment the high drift rate of Baetis was largely active.

Simulium is the most rheophilic of the 3 taxa, as they demonstrate a preference for water velocities above 20 cm s -1 (Tachet et al., 2010), are able to withstand high flows around 90 cm s -1 [START_REF] Finelli | Stream insects as passive suspension feeders: Effects of velocity and food concentration on feeding performance[END_REF], and can be found in harsh conditions with water velocities around 300 cm s -1 [START_REF] Phillipson | The effect of current speed on the distribution of the larvae of the blackflies, Simulium variegatum (Mg.) and Simulium monticola Fried (Diptera)[END_REF][START_REF] Wotton | The reaction of larvae of Simulium noelleri (Diptera) to different current velocities[END_REF]. Attached to rocks, they feed by catching fine particles in the water with their filter mandibles. Adapted to high water velocities, they drift more in slow than in fast waters [START_REF] Fenoglio | The impacts of increasing current velocity on the drift of Simulium monticola (Diptera: Simuliidae): a laboratory approach[END_REF]. In our experiment, they displayed the lowest drift rate and there was no significant relationship between their drift propensity and the tested water velocity gradient, which led us to presume that they drift actively. Indeed, exposed to experimental floods, [START_REF] Fingerut | Seeking shelter from the storm: responses of benthic stream invertebrates to natural and experimental floods[END_REF] showed that larval benthic densities of Simulium declined in microhabitats with near-bed velocities above 100 cm s -1 , much higher than velocities recorded under our conditions.

Chironomus is quite ubiquitous along the water velocity gradient (Tachet et al., 2010) and in the present experiment few individuals drifted under the lowest flow conditions. In running waters, they usually live buried in the substrate or within cocoons feeding on fine particulate organic matter [START_REF] Berg | Larval food and feeding behaviour The Chironomidae: Biology and Ecology of Non-biting Midges[END_REF]. Without any morphological adaptations to cope with the drag force of the current, Chironomus was the only taxon that drifted significantly more when the water velocity increased and then, exhibited passive drift. In the field, many authors reported that a high proportion of the drifting community is composed of Chironomidae [START_REF] Imbert | Drift and benthic invertebrate responses to stepwise and abrupt increases in non-scouring flow[END_REF]Kennedy et al., 2014;Robinson et al., 2004), which under flood conditions, respond as soon as the flow begins to increase. Despite this high sensitivity to flow, the analysis of longterm data (13-years) showed that chironomids are among the most resilient taxa of the community following catastrophic floods, in relation to their ecological traits (i.e. their abundance and their short life cycle - [START_REF] Woodward | Resilience of a stream community to extreme climatic events and long-term recovery from a catastrophic flood[END_REF]. [START_REF] Taniguchi | Effects of habitat complexity on benthic assemblages in a variable environment[END_REF] emphasised the role of shelter availability in invertebrate size distribution, with larger proportions of small individuals in complex habitats. Some works have highlighted the importance of gravel bed quality in limiting the impact of disturbances such as floods and flow variations [START_REF] Holomuzki | Sediment texture mediates high-flow effects on lotic macroinvertebrates[END_REF][START_REF] Long | The effects of water velocity and sediment size on Acroneuria abnormis (Plecoptera: Perlidae) entrainment[END_REF]. We did not find any evidence of gravel bed quality impact on drift rate. As Baetis and Simulium drifted actively, habitat could not mitigate the effects of water velocity on the passive drift of these two taxa.

However, considering that Chironomus are passive drifters, the lack of relation between drift rates and gravel bed quality could indicate that gravel bed quality was not different enough to induce a distinction between sheltering opportunities. Perhaps this result is due to the fact that we worked with small larvae (Figure S1 and S2) and that the size of the interstices provided by the fine gravel bed was too large to effectively limit sheltering opportunities.

No significant relationship between drift and head capsule width (HCW) was observed for Baetis, while Simulium and Chironomus HCW data showed a clear trend for larvae with small HCW to drift more. This could possibly be linked to a reduction in the ability of young individuals to access shelter due to lower locomotor abilities, at least in Chironomus larvae.

Concerning Simulium, we emphasized that water velocities underwent in the present experiment were much lower than their preferred water velocity [START_REF] Fingerut | Seeking shelter from the storm: responses of benthic stream invertebrates to natural and experimental floods[END_REF].

Presumably, their drift was then active. Environmental conditions were good but low organic matter in the experimental channels may have motivated voluntary drift. It may have concerned in priority small Simulium since they need higher organic concentration than big ones to optimize their growth [START_REF] Charpentier | Effect of current velocity on ingestion rates of black fly larvae[END_REF]. Baetis are good swimmers, especially compared to the two Diptera used in this experiment. The lack of significant relationship between HCW and drift appears consistent with their swimming abilities, which may have

given Baetis similar opportunities to reach shelters regardless of their developmental stage.

When considering BL, results showed similar patterns to HCW, except for Chironomus (see Figure S1). Head capsule width is highly correlated with size in Baetis and Simulium but not in Chironomus. In Chironomus, the relationship between larval stages and body length is weak [START_REF] Richardi | Determination of larval instars in Chironomus sancticaroli (Diptera : Chironomidae) using novel head capsule structures[END_REF]. Thus, ontogenesis could be more relevant than larval body size in favouring drift for Diptera larvae. As far as we know, there is no literature linking invertebrate size to drift, except for daylight/night drifting differences. Drift rate of small individuals is higher during daylight while larger individuals drift more at night [START_REF] Stewart | Drift of Ephemeroptera and Plecoptera in two Colorado rivers[END_REF][START_REF] Allan | The size composition of invertebrate drift in a rocky mountain stream[END_REF][START_REF] Bowles | Size composition of invertebrate drift and fish predation in a Texas stream[END_REF], suggesting that small individuals are more likely to experience passive drift than large ones. In the present experiment, daylight and night-time drift levels were quite similar, but they were not interpreted because of the progressive depletion of invertebrates through time.

Unexpectedly, a seasonal effect on the drift of Baetis and Simulium was detected. Baetis drifted more in fall than in spring, while the opposite was observed for Simulium. The mean water temperature was 2°C warmer in fall, and because invertebrates are ectotherms, this resulted in an increase in metabolism and activity. In addition, [START_REF] Kohler | Positioning on substrates, positioning changes, and diel drift periodicities in mayflies[END_REF] emphasised an increase in the feeding intensity in fall with Baetis more frequently observed on the surface of stones and more prone to drift. For Simulium, little information is available on their propensity to drift according to season. [START_REF] Stoneburner | Seasonal fluctuations of macroinvertebrate drift in a south carolina piedmont stream[END_REF] examined the composition of the drift over the course of a year in a California stream, and noted two peaks for Simuliid densities in drift.

Two species of Simuliid were identified with one drifting preferentially in early spring and the second in fall, and these differences were assumed to be driven by their emergence dates. As

Simuliids collected for our experiment were larger in March than in October (Figure S2), their higher drift rate in March might possibly be related to a time proximity with their emergence date at that time.

The present experiment was conducted in a closed system. This allowed natural processes such as immigration or the hatching of invertebrates to be avoided, which would have prevented the estimation of the drift entry rate. Ranging from 12 to 43 cm s -1 , water velocities obtained in this experiment are representative of conditions in the natural environment. Baetis and Simulium drifted actively and conditions were not stringent enough to induce significant passive drift.

Ranging from 0.36 to 7.34 dynes cm -2 , shear stress experienced by invertebrates in our channels was lower than the 9 dynes cm -2 threshold needed to dislodge the invertebrates and to induce passive drift [START_REF] Gibbins | When is stream invertebrate drift catastrophic? The role of hydraulics and sediment transport in initiating drift during flood events[END_REF]. Small Simulium demonstrated an abundance of drift, probably through active drift with low velocities driving the movement of individuals. In contrast, Chironomus, and specifically young stages, drifted passively.

This work provides the first accurate estimates of invertebrate drift entry rate. The accuracy of these rates are important because they allow the impacts of disturbances on invertebrate communities to be predicted, and they improve models of trophic interactions, such as those for drift-feeding fishes (Hayes et al., 2007).

Supporting Information

Table S1 Step Concerning Simulium, all the estimated parameters were negative (Table 1.2). The effect of HCW at low velocity (γS1) was significant (P(γS1 > 0) = 0.000), while the effects at medium (γS2) and high velocities (γS3) were around the threshold of significance (P(γS2 > 0) = 0.122; P(γS3 > 0) = 0.096). γS1 was significantly different from both γS2 and γS3 with probabilities of 0.022 and 0.030 respectively. γS2 and γS3 were equal (P(γS2 > γS3) = 0.551). Small Simulium drifted more at low water velocities than at medium or high water velocities (Figure 1.1).

Concerning Chironomus, all the estimated parameters were negative (Table 1.1) but only γC2 and γC3 were significant (P(γC1 > 0) = 0.468; P(γC2 > 0) = 0.048; P(γC3 > 0) = 0.013). γC2 was not different from γC3 and γC1 (P(γC1 > γC2) = 0.887; P(γC2 > γC3) = 0.605) but γC1 was significantly higher than γC3 (P(γC1 > γC3) = 0.939). There was no relationship between the head capsule width of invertebrate and the drift probability when the water velocity was low.

However, small individuals drifted more in medium and high flow conditions (Figure 1.1).

Results of this model supported the hypotheses proposed in the MS about the effect of individual head capsule width on the drift of invertebrates.

Chironomus

Low velocity Medium velocity High velocity

According to the present experiment, moderate water velocity induces passive drift of invertebrates even if bed load transport is not initiated. Then, besides the effect of catastrophic hydrological events that are known to deplete the benthos, an increase in the frequency of moderate floods may also affect their abundance.

For fish, the increase in water velocity increases the energetic costs to maintain hunting positions. They would possibly take advantage of an increase of food (especially Chironomus)

up to a threshold (25 cm s -1 according to [START_REF] Heggenes | Downstream migration and critical water velocities in stream channels for fry of four salmonid species[END_REF] for alevins). However, it remains to be calculated if the increase in swimming expenditure is balanced by both the increase of energy intake and the decrease of the hunt efficiency. In anyway, at higher water velocities, they will try to shelter the time of the flood, without benefiting from the drifting invertebrates.

Then if the increase in floods frequency forecasted by Global Climate Change exacerbates the risk of depletion of benthic invertebrates it should lead to a decrease in food availability for carnivorous fish. This may especially affect 0 + young salmonids for at least 4 reasons: i/ they start exogenous feeding in spring when the risk of flood events is still high (and this risk will increase with GCC), ii/ they are constrained by the small size of their oesophagus to small sized prey, iii/ they have much less reserve that large fish to face starvation periods, iv/ their swimming ability is limited at high water velocity.

Chapter II Effect of feeding conditions and temperature on growth and metabolism of alevins

Study context

The previous chapter highlighted that the increase in water velocity and flood frequency due to Global Climate Change should increase invertebrate drift (and especially of Chironomus). This might result in the depletion of the benthic invertebrate community, and on the drifting one as drifting invertebrates come from the benthos. For drift feeding fish, this means an increased risk to face periods of food scarcity.

When salmonid alevins emerge, they have almost exhausted the reserves of their yolk sac and they need to start feeding quickly [START_REF] Johnson | Movement and feeding ecology of recently emerged steelhead in Lake Ontario tributaries[END_REF][START_REF] Ladago | Early feeding by lake trout fry[END_REF]. For many fish species, starvation during larval stage induces high mortality rates [START_REF] Hunter | Feeding Ecobgy and Predation of Marine Fish Larvae[END_REF][START_REF] Jonas | Relative importance of direct and indirect effects of starvation for young walleyes[END_REF] and it is an important driving force for 0 + salmon (Salmo salar) survival (Kennedy et al. 2008) and growth (Ward et al. 2009). In the wild, most species regularly face periods of food deprivation and besides behavioural responses, they may adapt to the quantity of resources available by lowering their metabolism (McCue 2010). The basal metabolism of an organism, its environment (temperature) and the amount of its body resources (usually lipids), are important factors acting on its ability to face long period of starvation.

Whatever the organism, at the beginning of starvation, pancreas increases the secretion of glucagon and diminish the secretion of insulin, which trigger catabolism in the liver and mobilisation of reserves [START_REF] Sundby | Plasma insulin, glucagon, glucagon-like peptide and glucose levels in response to feeding, starvation and life long restricted feed ration in salmonids[END_REF]. First, liver degrades glycogen in glucose via glycogenolysis pathway. Carbohydrates are then used by the brain and carbohydrate-dependent cells (e.g. red blood cells). However, salmonids are carnivorous fish and they have adapted their anatomy, physiology and metabolism to their natural diet, which contains few or no carbohydrates [START_REF] Kamalam | Utilisation of dietary carbohydrates in farmed fishes: New insights on influencing factors, biological limitations and future strategies[END_REF]. They mainly synthesise glucose from non-carbohydrate precursors via gluconeogenesis (i.e. lactate, pyruvate and amino acids -NRC 2011). When Temperature is an important factor that regulates the metabolism of organisms, especially in poïkilotherms, which do not regulate their internal temperature. Warmer temperatures increase their metabolism and so the reserve consumption rate. Therefore, Global Climate Change might affect young trout growth and survival through both a reduction in prey availability and an increase in metabolic loss. Thus in that chapter, we conducted an experiment in order to better understand the interactions of these two factors (prey availability and temperature) on young trout growth and metabolic pathways.

Introduction

Climate Change (CC) will alter precipitation patterns (IPCC 2013) and will lead to an increase in winter/spring rainfall in most coastal areas of the north Atlantic. In freshwater ecosystems, rains are the primary source of running water discharge in rivers (Giller & Malmqvist, 1998) and with heavier and more frequent rainfall predicted, flooding events will become more frequent and intense in riverine systems. Further, CC will increase temperatures globally (IPCC 2013), which will affect metabolic rates, vital activities and growth of ectotherm organisms (Allan & Castillo, 2007).

Brown trout (Salmo trutta Linnaeus) alevins start their exogenous feeding in spring, which usually coincides with high prey availability (Romaniszyn et al., 2007). When the young trout emerge from the gravel, they have a limited amount of reserves and thus need to start feeding quickly to avoid mortality. Emergence is described as a critical period (Elliott, 1994), where

high mortality rates occur (Elliott, 1986). More recent studies demonstrated that starvation during the critical period was an important driving force for 0 + salmon (Salmo salar) survival rates (Kennedy et al., 2008) and growth (Ward et al., 2009). The effect of temperature on metabolism is another factor affecting the survival of emerged salmonids. Régnier et al. (2013) showed that metabolic rate increased by 1.4-1.6 in response to a 4°C temperature increase for hatched brown trout. Thus, CC might affect young trout growth and survival through both a reduction in prey availability, as flood frequency and magnitude have the potential to drastically reduce the invertebrate community (Robinson et al., 2004), as well as through an increase in metabolic loss driven by rising temperatures.

While salmonid growth and survival at the swim-up stage have been relatively well studied [START_REF] Bilton | The Effects of Starvation and Subsequent Feeding on Survival and Growth of Fulton Channel Sockeye Salmon Fry (Oncorhynchus nerka)[END_REF][START_REF] Twongo | Significance of the Timing of Initial Feeding in Hatchery Rainbow Trout, Salmo gairdneri[END_REF][START_REF] Koss | Influence of the Timing of Initial Feeding on the Survival and Growth of Hatchery-Reared Atlantic Salmon (Salmo salar L.)[END_REF][START_REF] Edsall | Starvation Resistance in Lake Trout Fry[END_REF], empirical data to assess the interaction between temperature and first-feeding are sparse.

In addition, metabolic pathways activated at a molecular level by the starvation of early life stages have not yet been described.

The aims of the present study were to better understand young brown trout response to CC by

(1) describing the consequences of starvation on their nutritional status, (2) testing their ability to recover from late first feeding and (3) analysing the effect of temperature on these phenomena (starving/recovering) by measuring growth and mRNA levels of genes involved in fatty acid/amino acid catabolism, as well as proteasomal and autophagy pathways.

Material and methods

Experimental features

Experimentation Forty-two tanks (LxWxH: 50x25x30 cm), each equipped with an individual pump and a Perlon filter, were filled to 20 cm height with filtered water from the Nivelle River. Three pebbles (ø:

4-5 cm) were placed in each tank to serve as a place for the fish to shelter. Twenty one tanks were settled in each of two thermo-regulated rooms that were at 8°C (± 0.1 in the water) and 11°C (± 0.4 in the water). Natural nyctemeral light-dark regime was simulated, with a 6:30 to 19:00 lit period and two 30 min periods of gradual light intensity change mimicking dawn and dusk.

Fish sampling

On 30/12/2014, eggs were obtained through the artificial fertilization of gametes of wild brown trout caught in the Nivelle watershed (43°21' N, 1°33' W). Eggs and alevins were reared at 9.36°C (± 1.18) until complete yolk sac depletion (790 degree-days). Survival was high (up to 82%) and on 30/03/2015, 126 alevins were individually weighed and photographed under binocular (x 10), allowing for individual identification thanks to melanophore distribution patterns (Garcia de Leaniz et al., 1994). Each of the 42 tanks was allocated with a random batch of 3 fish. Ten additional alevins were settled in a bucket for 24h at each temperature in aerated water and killed on Day 0 (D0). Live Chironomid larvae (Grebil, Arry, France) were distributed ad libitum from D0 in 9 tanks at each temperature. Every morning, leftover food was removed and counted, and a known number of new larvae were then added. All tanks (fed F and starved S) received the same pipette disturbance from the feeding. On D5, fish from 3 tanks from the fed (F5) and starved conditions (S5) at each temperature were measured, weighed, photographed and subjected to lethal anesthesia before being frozen in liquid nitrogen and stored at -80°C until analysis. Fish from another 3 tanks from the starved condition received food ad libitum (Delayed-feeding, DF). From D5 to D9, 6 fed tanks (F9), 6 unfed tanks (S9) and 3 delayedfeeding tanks (DF9) remained. As some mortality occurred at D9, the experiment was stopped.

Relative quantification of mRNAs levels for catabolic genes mRNA levels were determined by quantitative real-time RT-PCR. The extraction of total RNA from whole alevins was performed using the Trizol reagent (Invitrogen, Carlsbad, CA, USA).

One microgram of total RNA was used for cDNA synthesis. The SuperScript III RNaseH-Reverse Transcriptase Kit (Invitrogen) with random primers (Promega, Charbonniéres, France)

were used. Primer sequences to amplify specific trout target genes [START_REF] Seiliez | Feeding Status Regulates the Polyubiquitination Step of the Ubiquitin-Proteasome-Dependent Proteolysis in Rainbow Trout (Oncorhynchus mykiss) Muscle[END_REF][START_REF] Seiliez | Amino Acids Downregulate the Expression of Several Autophagy-Related Genes in Rainbow Trout Myoblasts[END_REF][START_REF] Panserat | Muscle Catabolic Capacities and Global Hepatic Epigenome Are Modified in Juvenile Rainbow Trout Fed Different Vitamin Levels at First Feeding[END_REF] are shown in the Supporting information (Table S1). Each PCR assay (Lightcycler 480, Roche Diagnostics, Neuilly-sur-Seine, France) included replicate samples (duplicates of reverse transcription and PCR amplification) and 2 negative controls (one with RNA but without RT enzyme and the other without RNA). For the analysis of mRNA levels, relative quantification of target gene expression was performed using the ΔCT method [START_REF] Pfaffl | A New Mathematical Model for Relative Quantification in Real-Time RT-PCR[END_REF]. The relative gene expression of Luciferase was used for the normalization [START_REF] Marandel | Remodelling of the Hepatic Epigenetic Landscape of Glucose-Intolerant Rainbow Trout (Oncorhynchus mykiss) by Nutritional Status and Dietary Carbohydrates[END_REF].

Statistical analyses

Total length was calculated as the average of measurements from 3 different operators to the nearest 0.1 mm using ImageJ (Abràmoff et al., 2004). Weight gain (Wg) and length gain (Lg)

were then calculated (eq.1):

𝐺𝑎𝑖𝑛 = 𝐹𝑖𝑛𝑎𝑙 -𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐼𝑛𝑖𝑡𝑖𝑎𝑙

Wg and Lg were analysed using nonparametric Wilcoxon-Man-Whitney rank sum test.

Molecular analyses were performed on 72 individuals, 6 fish per condition and per temperature on D0, on D5 (F5 and S5) and on D9 (F9, S9, and DF9). mRNA levels were analysed using a Bayesian modelling approach with OpenBUGS®. Data were analysed separately according to temperature. If Xi is the expression of the i th individual, we assume:

𝑙𝑜𝑔(𝑋 𝑖 ) ~ 𝑁𝑜𝑟𝑚𝑎𝑙(𝜇 𝑖 , 𝜎) 𝜇 𝑖 = 𝜇 + 𝛼 𝑗[𝑖]
Where the mean of the log expression μi was modelled according to a global average (μ) and αj[i] was a fixed-effect parameter for rearing conditions.

Bayesian computation

Parameters were given independent 'non informative' priors (i.e., sampled in a normal distribution N(0, 100) and for σ in a half-Cauchy t(0, 1, 1) distributions -Gelman & Hill 2006).

The convergence of three Markov Chain Monte Carlo (MCMC) chains to their ergodic distribution was tested for the model parameters via the Gelman-Rubin (GR) diagnostics. The first 10 000 iterations were discarded and then, 10 000 further iterations (thin = 10) were performed.

Results

Survival

No death was recorded in the fed group, and the same was true for all groups up to D8. On D9, 12 fish died: 4 at 8°C (S9), 8 at 11°C (7 S9 + 1 DF9). The experiment was stopped and dead fish were not considered in further biometric and molecular analyses.

Biometric data

As expected, fed fish gained weight and starved fish lost weight (Fig. 1a). If weight gain on D9 was higher than on D5 for fed fish, no difference occurred among starved fish between the two dates. Temperature did not change weight gain in fed fish. On the contrary, for starved fish weight loss was significantly increased by temperature increase (Wilcoxon test, p-value(S5) and (S9) < 0.01). The same tendency was observed for delayed fed fish (p-value(DF9) = 0.059). DF9

fish exhibited intermediate values between S9 and F9.

At the start of the experiment, fish measured 2.584 cm (± 0.037) and weighed 0.106 g (± 0.003), (box-plots, Fig. 1b). On average, F9 fish were longer and heavier than S9 ones, and DF9 were in between, similar to results for weight gain (Fig. 1a). Weight/length relationships did not differ much among rearing conditions (Fig. 1b). However, it was noteworthy that at 11°C fish were usually shorter than at 8°C for a similar weight (Fig. 1b). Length gain values confirmed this point with higher gains at low temperature (8°C vs 11°C) for F9 and DF9 conditions (Wilcoxon test, p-value(F9) = 0.01; p-value(DF9) = 0.046; p-value(S9) = 1).

During the feeding period, DF9 fish ate as much as the fed fish (F5 and F9), but they grew less, especially at 11°C (Fig. 1c and1d). For F5 fish, the mean number of Chironomus eaten daily almost doubled at 11°C (8.08 on average vs 4.67 on average at 8°C) but it was not correlated with a better performance in growth. Results for F9 showed the same trend (4.67 prey on average at 8°C and 8.08 at 11°C for a similar growth). mRNA levels for catabolic genes All mRNA levels were usually above 0.5, except in some cases for genes involved in fatty acid catabolism at 11°C, and in proteasome and autophagy in fed fish (Table S2). Many of the differences between feeding conditions were found for genes involved in autophagy and proteasome pathways (Table 1, FC1 to FC8). On D5 and at 8°C, starvation is associated with significantly higher mRNA levels for 4 of the 5 tested genes involved in proteasome and for 1 of the 5 tested involved in autophagy (Fold change, FC1 < 1, Table 1). At 11°C, mRNA levels for all the monitored proteasome/autophagy-related genes were significantly higher in S5 than in F5 (Table 1, FC2 < 1). On D9, mRNA levels for autophagy and proteasome genes in S9 were higher than in F9 regardless of the temperature (FC3-FC4). For fish that were starved for 5 days before feeding (DF9), mRNA levels for genes coding for autophagy and proteasome were inbetween, i.e. they were more expressed than those in F9 (FC5 and FC6) but less than those in S9 (FC7 and FC8). In a comparison of gene expression at different temperatures, FC values were significant (0.9 < P < 1) in half of cases, and in all except one, mRNA levels were higher at 11°C (FC9 to FC13).

Concerning fatty acid catabolism (HOAD and CPT1 genes), FC values in mRNA levels between starved and fed fish were inconsistent between D5 and D9 (Table 1, FC1C2/C3C4). On D5, mRNA levels were higher in starved fish when compared to fed fish (significant FC < 1), while lower on D9 (significant FC > 1). Results for amino acid catabolism genes (GDH, ASAT, ALAT genes) were more congruent, indicating higher mRNA levels for these genes in fed fish compared to starved fish on both D5 and D9. For FC values in delayed-feeding (DF) fish and fed (F) fish (FC5-6), no significant values were reached for genes involved in fatty acid catabolism. Looking at amino acid catabolism, significant FC values were above 1 at 8°C, while they were less than 1 at 11°C, suggesting that mRNA levels for amino acid catabolic genes were higher in fed fish than in delayed fed fish at low temperature, while the opposite was observed at 11°C.

Comparing DF9 fish with S9 fish (Table 1, FC7-8) at 11°C, almost all of the mRNA levels for genes involved in catabolism (8 among 9 tested) were higher in DF9. At 8°C only 2 differences for amino acid catabolism genes were observed, also in favour of S9.

Concerning the impact of temperature, significant FC values (0.9 < P < 1) were observed in a quarter of cases, and all were > 1, suggesting that genes were expressed at a higher level at 8°C than at 11°C.

Discussion

This experimental study is the first to describe the influence of temperature and food depletion on mRNA levels of genes involved in the nutritional status of brown trout at the time of the first exogenous feeding. The transition from yolk reserves to exogenous feeding is a critical period for many fish, and may lead to high mortalities (Kennedy et al., 2008). Our data have clearly shown that in case of starvation, genes involved in autophagy and proteasome were highly expressed and it was boosted at 11°C. Surprisingly, the expression of genes coding for fatty acid and amino acid catabolism was not higher during starvation at 11°C (Table S2). These results are counter-intuitive and suggest that the regulation of these catabolic pathways may occur at different levels (e.g. post-transcriptional regulation; [START_REF] Salem | Effect of Starvation on Global Gene Expression and Proteolysis in Rainbow Trout (Oncorhynchus mykiss)[END_REF], or that some other mechanisms such as hypometabolism are at work. This study has also demonstrated that young brown trout were able to feed and grow after 5 days of starvation. However, after this short period of starvation, mortalities were recorded at 11°C but not at 8°C, suggesting that in the wild, recovery would likely be uncertain at this temperature, even if food availability was restored. Finally, results demonstrated that at 8°C, food was more efficiently converted to tissue than at 11°C.

Energetic stress indicators: autophagy and proteasomal pathways at a molecular level

During nutrient restriction, metabolism changes to provide cellular energy via catabolic processes. Autophagy and proteasome are induced to mobilise energy and most often occur when essential nutrients are limited [START_REF] Wing | Increase in Ubiquitin-Protein Conjugates Concomitant with the Increase in Proteolysis in Rat Skeletal Muscle during Starvation and Atrophy Denervation[END_REF][START_REF] Mizushima | Autophagy : Process and Function[END_REF]. To provide metabolic fuel, autophagy degrades all potential sources of energy (proteins, lipids and glycogen) and proteasome degrades skeletal-muscle proteins. In this regard, an induction of the expression of genes involved in both pathways can indicate energetic stress. The mRNA levels were greater in fish that were starved for 5 days and these differences were exacerbated at 9 days, highlighting the metabolic distress of starving fish. Consequences of starvation were still noticeable in delayed-fed fish although food input drove the majority of them back to a normal state. These results confirm that both pathways (autophagy and proteasome) are regulated by the feeding status [START_REF] Robinowitz | Autophagy and Metabolism[END_REF][START_REF] Seiliez | An in Vivo and in Vitro Assessment of Autophagy-Related Gene Expression in Muscle of Rainbow Trout (Oncorhynchus mykiss). Comparative biochemistry and physiology[END_REF]. Moreover, temperature exacerbates stress induced by starvation probably in relation with a higher standard metabolic rate (Régnier et al., 2013). More energy is needed to maintain vital functions and body reserves of alevins are probably rapidly depleted, which would explain a higher induction of these catabolic pathways at 11°C, while recovery was not boosted at a sufficient rate to compensate when food was available again.

Recovery from delayed first exogenous feeding

Introduced by [START_REF] Blaxter | The Influence of Egg Size on Herring Larvae (Clupea harengus L.)[END_REF], the "point-of-no-return" (PNR) is the threshold after which fish are still alive but too weak to feed. At 8 °C, no delayed-fed fish died and they were in an intermediate state, having gained weight when compared with starved fish, but exhibiting lower performance than fed fish. PNR value is largely dependent on temperature [START_REF] Mcgurk | Effects of Delayed Feeding and Temperature on the Age of Irreversible Starvation and on the Rates of Growth and Mortality of Pacific Herring Larvae[END_REF][START_REF] Dou | Feeding Resumption, Morphological Changes and Mortality during Starvation in Japanese Flounder Larvae[END_REF]. At 11°C, starvation over a 5-day period was an intense stress and all fish were not able to recover. At D9, for delayed-fed survivors, results were mixed. Some gained weight, but some still had the same weight loss levels than starved fish, suggesting that they reached the PNR and would not be able to recover. Fish can exhibit compensatory growth after a period of food deprivation [START_REF] Nicieza | Growth Compensation in Juvenile Atlantic Salmon : Responses to Depressed Temperature and Food Availability[END_REF], but the duration of the present experiment was probably too short to detect such a phenomenon. However, there were no evidences from prey consumption rate and catabolism that a catch-up growth may arise.

Feed utilisation at different temperatures

Because brown trout are ectotherms, their growth is linked to temperature. The best energy conversion efficiency into growth for Salmo trutta is around 9-10°C [START_REF] Marr | Influence of Temperature on the Efficiency of Growth of Salmonid Embryos[END_REF][START_REF] Blaxter | Development: Eggs and Larvae[END_REF][START_REF] Elliott | Modelling Growth of Brown Trout, Salmo Trutta, in Terms of Weight and Energy Units[END_REF]. Our results demonstrated a better food conversion in growth efficiency at 8°C when compared to 11°C (similar weight gain and catabolism, even though feed intake was higher at 11°C). In addition, length gain was higher at 8°C. Similar results for another salmonid were found by [START_REF] Malzahn | Temperature Effects on Growth and Nucleic Acids in Laboratory-Reared Larval Coregonid Fish[END_REF] who highlighted hyperplasia phenomenon leading to longer coregonid fish in colder water.

In the context of CC, an increase of 3.2°C in air temperature will produce a moderate but sensible increase in stream water temperature from 1 to 2°C (Bal et al., 2014). Present results suggest that this may cause higher mortality rates during the critical period of emergence, especially when in conjunction with food shortage which is not a scarce event in the wild (Kennedy et al. 2008). Besides its effect on water temperature, CC will affect flow through changes in precipitation patterns. In winter and spring, higher flows will likely magnify the effect of temperature by reducing the availability of invertebrates both through habitat reduction (Kennedy et al. op. cit.) and invertebrate washout.

Table S1: Primers used for real time RT-qPCR analysis.

Pathways

Further considerations and discussion

In the present paper, analyses were focused on averages of mRNA levels according to conditions, while variance was not commented as an informative factor by itself because of the paper format chosen (brief communication). Because I thought it might brought interesting developments, I include this analysis here. After 9 days of experiment, standard deviation values of mRNA levels of genes coding for fatty acid and amino acid catabolism ranged from 0.09 to 0.42, while standard deviations of genes involved in proteasome and autophagy ranged from 0.11 to 1.99 (Figure 2.2). The important standard deviations of genes involved in proteasome and autophagy indicate that individual response to the treatment varied a lot.

Considering weight as a reveller of stress induced by starvation (the individuals suffering most from starving being those losing more weight), we tested if the induction of these genes correlated with the weight. We hypothesised that the alevins suffering most from starvation should be those who had lost more mass and that they should be the individuals in which the expression of genes involved in proteasome and autophagy was maximized. Considering only data of the 9 th day, correlations were tested with OpenBUGS® with estimations of coefficients for each j condition (F9, S9 and DF9). If Xi is the mRNA level of the i th individual, we assumed:

𝑋 𝑖 = 𝑎 𝑗[𝑖] 𝑊𝑔 𝑖 + 𝑏 𝑗[𝑖]
With aj[i] the slope coefficient and bj[i] the intercept, both estimated according to j conditions.

Individual weight gain (WGi) was calculated as follows: When the fit was between 0.5 and 1, the correlation between mRNA levels and WG was good.

When the fit was between 0 and 0.5, the correlation was intermediate and when the fit is negative, there was no correlation. For each condition (fed F9, starved S9 and delayed firstfeeding DF9), we tried to link individual weight gain with the expression of the autophagy and proteasome related genes. The fit represented the strength of the relationship, then a represented the direction of the relationship and b the intercept. A total of 30 relationships were tested (3 conditions x 10 genes).

For fed fish (F9), considering 10 correlations between individual weight gain and mRNA levels, 8 fits were negative (Table 2.2). The majority of the parameters (7/10 intercepts and 8/10 slope coefficients) were equal to 0. This means that mRNA levels of genes involved in these pathways were very low and no relationship between weight gain (WG) and mRNA levels was observed.

For starved fish (S9), fits were intermediate, ranging from 0 to 0.30. 7 slope coefficients (a)

were significantly lower than 0. For delayed first-feeding fish (DF9), 9 fits were good, over 0.44 (except Mul1). 9 slope coefficients (a) were significantly lower than 0. It appeared that when fish face periods of food deprivation (S9 and DF9), the more they lost weight, the more genes involved in proteasome and autophagy were expressed. Moreover, whatever the gene considered, F9 and S9 constituted two distinct groups of individuals, while DF9 made the link between F9 and S9 (Figure 2.3). The results confirm that mobilisation of protein stored in muscles results in a loss of weight [START_REF] Sandri | Autophagy in health and disease. 3. Involvement of autophagy in muscle atrophy[END_REF]. Late access to food (DF9) would induce a decrease in proteolytic activity for some alevins which would probably come back to a normal metabolic activity and survive, but the DF alevins with still high mRNA levels for these genes on the 9 th day would probably die in the following hours/days. Further analyses measuring the expression of these genes on alevins in the wild could help to assess the starving situation of 0 + fish. Indeed, if it is possible to use them as markers of metabolic stress, they would be an indicator of the alevin access to trophic resources.

According to the results of the present experiment conducted in controlled environment, alevins can survive only a 9-day period to starvation once the yolk sac is exhausted and warmer temperature clearly intensifies the metabolic distress. Consequently, survival and growth of salmonid alevins would be threatened by Global Climate Change because of the increase in starvation risk. However, these results should be considered with caution and may be difficult to transpose to the wild due to additional factors such as the cost of swimming, the risk of predation and the impact of competition and territoriality that may also increase. A desynchronization of species phenology could occur with a limited quantity of prey when alevins need to feed (i.e. the Match-mismatch hypothesis; Cushing 1990). Additionally, behaviour of alevins such as foraging activity, aggressive behaviours and dominance relationships could be modified by the scarcity of prey [START_REF] Keeley | An experimental analysis of territory size in juvenile steelhead trout[END_REF].

Introduction

During reproduction, females dig nests in the gravel, drop hundreds to thousands of eggs immediately fertilised by males and they cover the clutch with gravel. As the development of the eggs depends largely on temperature, individuals of the same clutch are subjected to the same conditions and hatch at the same time. Similarly, the emergence is quite synchronous between the individuals of the same clutch but also between the clutches laid simultaneously.

Consequently, densities of just-emerging alevins can be very high [START_REF] Jenkins | Effects of population density on individual growth of brown trout in streams[END_REF]). As salmonids are territorial species, alevins establish territories progressively [START_REF] Grant | Territoriality. Behavioural ecology of teleost fishes[END_REF]) and defend a feeding position to ensure a positive net energy balance and to optimise the energy gained. Territoriality and aggressive interactions can be noted as early as the first day after emergence (Kalleberg 1958) or 2.5 days after emergence [START_REF] Titus | Selection for growth potential among migratory brown trout (Salmo trutta) fry competing for territories: evidence from otoliths[END_REF]. The size of the territory is related to the size of fish and alevins defend small areas (0.1-0.2 m² -Grant & Kramer 1990; Grant et al. 1998). However, the territory size and the intensity of the competition are influenced by other factors including competitor density, resource density and resource distribution in space and time [START_REF] Brown | The evolution of diversity in avian territorial systems[END_REF]. The timing of the emergence plays an important role in competition and alevins emerging early can settle in more profitable position than congeners emerging later [START_REF] Metcalfe | Early predictors of life-history events: the link between first feeding date, dominance and seaward migration in Atlantic salmon, Salmo salar[END_REF][START_REF] Einum | Maternal effects of egg size in brown trout (Salmo trutta): norms of reaction to environmental quality[END_REF]Johnsson et al. 1999). The threshold model of feeding territoriality [START_REF] Carpenter | Food abundance and territoriality: to defend or not to defend?[END_REF] predicts that animals defend feeding territories according to the food abundance: when the food is too scarce, territorial individuals have a lower fitness compared to non-territorial ones; on the contrary, when food is superabundant, non-territorial individuals gain the same amount of food as territorial ones without paying the cost of defence (Grant et al. 2002;[START_REF] Brännäs | Influence of food abundance on individual behaviour strategy and growth rate in juvenile brown trout (Salmo trutta)[END_REF][START_REF] Imre | The effect of food abundance on territory size and population density of juvenile steelhead trout (Oncorhynchus mykiss)[END_REF]).

The social hierarchy is organized by dominant individuals, subdominant and less aggressive, subordinate fish [START_REF] Adams | Alternative competitive strategies and the cost of food acquisition in juvenile Atlantic salmon (Salmo salar)[END_REF][START_REF] Sloman | Physiological effects of dominance hierarchies within groups of brown trout, Salmo trutta, held under simulated natural conditions[END_REF]. Generally, large individuals have the higher growth potential and dominate their congeners and the access to the best stream positions affording maximum potential profits [START_REF] Chapman | Aggressive behavior in juvenile coho salmon as a cause of emigration[END_REF]Mason & Chapman 1965;[START_REF] Fausch | Profitable stream positions for salmonids: relating specific growth rate to net energy gain[END_REF].

Indeed, high metabolic turnover rate is needed to allocate energy both to somatic growth and to territorial defence.

The diminution in prey availability would increase both territory size and foraging activity of alevins to increase encounter rate of prey [START_REF] Biro | Density-dependent mortality is mediated by foraging activity for prey fish in whole-lake experiments[END_REF]) and maintain a constant abundance of food [START_REF] Toobaie | Effect of food abundance on aggressiveness and territory size of juvenile rainbow trout, Oncorhynchus mykiss[END_REF]. This would result in alevins emigration or death and a decrease in fish density when food abundance is low. Aggressive behaviour of young brown trout impacts their growth and survival and influence significantly the population dynamics [START_REF] Biro | Density-dependent mortality is mediated by foraging activity for prey fish in whole-lake experiments[END_REF] and it is necessary to consider events occurring at the individual level to understand these regulatory mechanisms [START_REF] Titus | Territorial behavior and its role in population regulation of young brown trout (Salmo trutta): new perspectives[END_REF]). Salmonid abundance is related to the individual territory size, consequently, any environmental factor affecting territoriality should affect population density (Grant et al. 1998). Moreover, food availability for fish depends on the productivity of the system and it is established that productivity is low during winter and increases in spring when day length, brightness and temperature increase (Sumner & Fischer 1979;[START_REF] Nakano | Reciprocal subsidies: dynamic interdependence between terrestrial and aquatic food webs[END_REF].

So, in order to assess the interaction between flood and some components of the ecosystem phenology (emergence timing and invertebrate production) on young salmonid performances, we conducted two experiments at different timing (early and late spring). In a semi-natural stream, we put first-feeding fish in large cages (to assess performances) and in small cages (to monitor behaviour). Then, we compared invertebrate production as well as alevins performances and behaviour according to the occurrence or the absence of a hydraulic disturbance (artificial flood) just before alevins release.

We aimed to test the following hypotheses:

1. Flood should diminish the abundance of invertebrates, 2. Flood should diminish food availability for fish, which should reduce growth and survival of alevins, 3. Flood should lead to an increase in alevins activity to search for prey and a more intense competition between congeners, 4. Ecosystem productivity should increase between early and late spring.

Material and Methods

Experimental design

The Lapitxuri semi-natural stream is a 2.80 m wide channel divided into 13 reaches of 10 m long each. Water is diverted from a tributary of the river Nivelle located near the French-Spanish border in the upstream part of the Nivelle watershed. Lapitxuri tributary is a typical trout brook characterized by a good water quality. We realized two trials of the same experiment, one in early and one in late spring. The experiment took place in two reaches of the artificial stream (30 m² each) located in the middle of its course (60 m downstream the flow entry). Flow was kept constant during the whole experiment (60 L s -1 ). On 11/02/2016, three large cages (LxWxH: 100x100x50 cm -1 m²; growth cages) and two small cages (LxWxH: 50x25x50 cm -0.0125 m²; observation cages) were set up into each reach (Figure 1). The cages were buried 10 cm deep in the substrate. The particle size distribution was the same in all cages, i.e. cobbles and pebbles ranging from 1 cm to 5 cm in size. The upstream and downstream gates of the cages (facing the water flow) were kept open to let the invertebrates colonise the cages until the beginning of the experiment. When fish were placed, they were closed to prevent the emigration of fish. The mesh size of the cages was 1 mm (even in the buried floor), except on the upstream gate, which was 2 mm to allow entering small invertebrates. Inside the cages water velocity was 5.16 cm s -1 (SD = 1.44) and water depth 12.57 cm (SD = 1.45) on average during the experiment. The large cages were used to study alevins growth and survival, while the small cages were used to study alevins behaviour. The observation cages were installed in front of the window of two underwater observation rooms. In the upstream reach, observation cages were located on the left bank at 4 m from the upstream limit of the reach. In the downstream reach, observation cages were located on the right bank at 2 m from the upstream limit of the reach (Figure 1).

Two trials took place, one in March (early spring) and one in April-May (late spring). Reach 1 (upstream) was used as Control Reach, whilst Reach 2 (downstream) was used as Impacted Reach (flood simulation). In the early spring (first trial) two growth cages were used in the "Control" and in the "Flood" Reaches. They were taken out before the second trial. Only one growth cage per reach was used in late spring (second trial).

To simulate the effect of a scouring flood, the out-flow coming from the flexible hose of a motor-pump was directed downward with a 30° angle to the gravel for 10 seconds, disturbing the gravel on a 20 cm wide and 50 cm long area. The flooding operation started at the right bank of the upstream end of the reach 2, then the pump was displaced by 20 cm to the left and used again for 10 seconds. This procedure was repeated over the entire width of the reach (14 times) and such transects were made every 50 cm to cover the entire area of the reach. The use of the motor-pump increased the mean water velocity to 97.74 cm s -1 (SD = 29.42) at 50 cm in front of the outflow. These velocities are comparable to velocities measured in the Nivelle river during a Q10 flood (i.e. a flood that has a 10% chance to occur due to its high intensitysee Figure S1 and Table S1 in Supplementary Information). After the flood simulation, the gravel bed was flattened again.

Fish processing

First-feeding alevins were randomly split in batches: four fish per batch for the observation cages and thirty-two alevins per batch for the growth cages. All of them were individually weighted, measured (total body length, BL), photographed for individual identification using melanophore distribution patterns (Garcia de Leaniz et al. 1994) and released in control and impacted cage the same day, or the day after the artificial flood. Fish density in both growth and behaviour cages was 32 fish m -². At the end of the experiment, all the surviving alevins were manually recovered (after 22/21 days in the growth cages, and 21/15 days in small cages, Table 1) and left fasting 24h. They were then anaesthetised, individually weighted, measured (BL), photographed and subjected to a benzocaine lethal anesthesia.

In order to monitor individual behaviour, fish were marked with Visible Implant Elastomer (VIE) alongside the anal fin [START_REF] Olsen | An evaluation of visible implant elastomer for marking age-0 brown trout[END_REF]. Three days before fish release, a forty fish devoted to the observation cages were anaesthetised and tagged. Three fluorescent VIEs were used (Orange, Green and Blue), together with the non-fluorescent white colour (Figure 1).

On the release day, four fish per colour were chosen according to the quality of the mark. They were anaesthetised, weighted, measured and allocated to the four observation cages (each cage received four fish having a different VIE-tag colour). Behavioural surveys were done three times a day, every day during the establishment of the hierarchy (the first week) and every two days during the second week . Surveys were done around 10:00am, 1:00pm and 4:00pm. For each cage, the location of all fish was first noted on a tridimensional map of the cage. Then, focus observations started for each targeted fish for 5 minutes (successively) and the following behaviours were noted: (i) prevailing activity (2 states: resting on the substratum or swimming),

(ii) number of catch prey attempts, (iii) distance of capture (3 categories: short < 2 cm, medium = 2-5 cm, long > 5 cm), and (iv) number of attacks given (4 types: intimidation, charge, nip or chase - [START_REF] Adams | Exercise, agonistic behaviour and food acquisition in Arctic charr, Salvelinus alpinus[END_REF] or (v) reaction to attack (3 types: no reaction, fleeing, riposte).

Benthic invertebrate sampling

Immediately after the flood simulation (07/03/2016 and 21/04/2016) and at the end of the experiment (30/03/2016 and 12/05/2016), 3 invertebrate samples were randomly collected in growth cages. Sampling was carried out by burying a corer (ø: 13 cm) 10 cm deep in the substrate. Gravels and pebbles were then gently removed and the invertebrates in the pipe pumped, recovered in a 500 μm sieve and preserved in 70% ethanol. This was repeated 3 times per cage, providing 12 invertebrate samples for the early spring trial and 6 for the late spring one. Invertebrates were identified and assigned to different groups: Diptera, Trichoptera, Ephemeroptera, Mollusca, Coleoptera, Plecoptera, Oligochaeta, Crustacea and "Others".

The origin of the fish as well as the main environmental characteristics during each trial are summarized in Table 1. 10.58 (SD = 0.9) 12.33 (SD = 1.4)

Statistical analyses

Growth Cages

Abundance of invertebrates (number of individuals by sample) as well as the individual weight gain of alevins and fish productivity (sum of final weights) were analysed using a bayesian modelling approach computed with OpenBUGS®. We assumed that the variables of interest followed a normal distribution. Then, the mean of the variable (μ.Variable) was modelled according to three parameters (Table 2): a fixed-effect relative to the flood (α), a fixed-effect relative to the season (β) and a fixed-effect relative to the interaction flood*season (γ). The logit of the probability of alevins survival was modelled with these same three effects. Where μ is the mean of the logarithm of the abundance of invertebrates, α is a fixed-effect parameter for the impact of the flood, β is a fixed-effect parameter for the season and γ is a fixed-effect parameter for the interaction flood*season. The same modelling treatment was applied to the total abundance of invertebrates and to the abundance per group (i.e. Diptera, Trichoptera, Ephemeroptera, Coleoptera, Plecoptera, Oligochaeta, Crustacea, Mollusca, and "Others").

Survival probability of alevins

We assumed that the alevins could not escape from the cage and so, not recovered alevins at the end of each trial were considered dead. Then, if p.Si is the probability of survival of the i th alevin, we assumed:

𝑆 𝑖 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖(𝑝. 𝑆 𝑖 ) 𝐿𝑜𝑔𝑖𝑡(𝑝. 𝑆 𝑖 ) = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑖 + 𝛽 𝑆𝑒𝑎𝑠𝑜𝑛 𝑖 + 𝛾 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑖
Where μ is the mean of the logit of the survival probability, α is a fixed-effect parameter for the impact of the flood, β is a fixed-effect parameter for the season and γ is a fixed-effect parameter for the interaction flood*season.

Individual weight gain of alevins

All the alevins were weighted at the beginning of each trial (W.begi) and all the survivors were weighted at the end (W.endi). Photos allowed the individual recognition of alevins and then calculation of the individual Weight Gain (WGi):

𝑊𝐺 𝑖 = 𝑊. 𝑒𝑛𝑑 𝑖 -𝑊. 𝑏𝑒𝑔 𝑖 𝑊. 𝑏𝑒𝑔 𝑖 Then, we stated that WG followed a normal distribution with μ.WGi the mean and σ.WGj the standard deviation. We assumed:

𝜇. 𝑊𝐺 𝑘 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑖 + 𝛽 𝑆𝑒𝑎𝑠𝑜𝑛 𝑖 + 𝛾 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑖

Where μ is the mean of the individual weight gain of alevins, α is a fixed-effect parameter for the impact of the flood, β is a fixed-effect parameter for the season and γ is a fixed-effect parameter for the interaction flood*season.

Fish productivity

The final weights of alevins (W.endi) were summed to obtain the fish production of each j cage (Prod.Fishj).

𝜇. 𝑃𝑟𝑜𝑑. 𝐹𝑖𝑠ℎ 𝑗 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑗 + 𝛽 𝑆𝑒𝑎𝑠𝑜𝑛 𝑗 + 𝛾 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑗

Where μ is the mean fish production, α is a fixed-effect parameter for the impact of the flood, β is a fixed-effect parameter for the season and γ is a fixed-effect parameter for the interaction flood*season.

Determination of hierarchy

Only One Side Attacks (OSA) were considered to establish the hierarchy matrix, i.e. attacks that induced a fleeing reaction or a gently sink underneath and downstream the aggressor [START_REF] Katano | Aggressive behaviour and dominance relationships of the dark chub, Zacco temmincki with special reference to their individual recognition[END_REF](Katano , 1990;;[START_REF] Nakano | Intra-and interspecific dominance hierarchies and variation in foraging tactics of two species of stream-dwelling chars[END_REF]. Aggression Index (AI) was calculated for each alevin by dividing the number of OSA made (OSA+) by the number of OSA sustained (OSA-). Then, the AI was related to the positioning of the alevin in the cage and alevins were ranked. "A" was associated to the highest AI and an upstream positioning within the cage and referred to the dominant alevin. "B" was associated to the second highest AI and referred to the subdominant alevin. "C" and "D" was associated to the lowest AI and a downstream positioning within the cage and referred to the subordinates.

Behavioural analyses

Following emergence, fish try to establish territories and start to feed. They have to learn how to swim in the water current, how to catch prey, and the hierarchy take a few days to be established. Alevins became progressively active and began to hunt and interact with their congeners over time. Therefore, δ represented the increase in the probability to be active or in the occurrence of discrete events (i.e. attempt to catch prey or aggressive interaction), δ1 being the increase in control cages and δ2 the increase in impacted cages. Difference between δ1 and δ2 was tested. Moreover, in the same reach, the second cage was positioned 2 meters downstream the first one, then a fixed-effect parameter λ relative to downstream cage positioning was also considered (Table 3). Where μ is the average of the logit of the probability of alevins to be active, δ is a fixed-effect parameter for the increase in activity over time and λ is a fixed-effect parameter for the position of the cage.

If Huntk is the number of attempts to catch prey (the feeding activity) of an alevin during the k th survey of five minutes, we assumed:

𝐻𝑢𝑛𝑡 𝑘 ~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛( 𝜇. 𝐻𝑢𝑛𝑡 𝑘 ) log(𝜇. 𝐻𝑢𝑛𝑡 𝑘 ) = 𝜇 + 𝛿 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛[𝑘] 𝑇𝑖𝑚𝑒 + 𝜆 𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑘
Where μ is the average of the logarithm of the numbers of attempts to catch prey in 5 minutes, δ is a fixed-effect parameter for the increase of the feeding activity over time and λ is a fixedeffect parameter for the position of the cage.

During a 5-minute survey, all the aggressive acts were summed, even if the focused alevins was the victim. An intimidation, a charge, a nip or a chase were equally considered as aggressive acts. Then, if Aggk is the number of aggressive interactions during the k th survey of five minutes, we assumed:

𝐴𝑔𝑔 𝑘 ~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛( 𝜇. 𝐴𝑔𝑔 𝑘 ) log(𝜇. 𝐴𝑔𝑔 𝑘 ) = 𝜇 + 𝛿 𝐹𝑙𝑜𝑜𝑑[𝑘]+1 𝑇𝑖𝑚𝑒 + 𝜆 𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑘
Where μ is the average of the logarithm of the numbers of aggressive interactions in 5 minutes, δ is a fixed-effect parameter for the increase of the aggressive activity over time and λ is a fixedeffect parameter for the position of the cage.

Correlations between individual weight gain of alevins and (i) the number of surveys during which each alevin was actively swimming in the water column, (ii) the average number of attempts to catch prey by survey and (iii) the average number of aggressive interactions by survey were tested as previously described. Similarly, the number of surveys during which each alevin was actively swimming was linked to the average number of attempts to catch prey by survey and the average number of aggressive interactions by survey. Correlations were tested with OpenBUGS®. If Yj was the individual weight gain or the alevin activity, we assumed:

𝑌 𝑗 = 𝑎 𝐵𝑒ℎ𝑎𝑣𝑖𝑜𝑢𝑟 𝑗 + 𝑏
Where Yj is for each j alevin, the individual weight gain (WGj) or the activity of alevins, a is the slope coefficient, Behaviourj is the behaviour of interest and b is the intercept. Fit of correlation was calculated by dividing the variability unexplained by the variability observed in the data set, as follows:

𝐹𝑖𝑡 = 1 - 𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑎𝑡𝑎 𝑠𝑒𝑡
When the fit is between 0.5 and 1, the correlation is good. When the fit is between 0 and 0.5, the correlation is intermediate and when the fit is negative, there is no correlation.

Bayesian computations

Parameters were given independent "weakly informative" priors. Following recommendations of [START_REF] Gelman | Data analysis using regression and multilevelhierarchical models[END_REF], μ, α, β, γ, a, b, δ1, δ2 and λ were sampled in a normal distribution, N(0, 100). σlog.Abond and σProd.Fish were sampled in a half-Cauchy truncated distribution, t(0, 1, 1). Hyperparameters of σ.WGj (B.σ.WG and E.σ.WG) were sampled in a gamma distribution, gamma(1, 1). We used each time three independent chains, the first 10 000 iterations were discarded as an initial burn-in period. Then, 10 000 further iterations (resulting from one every ten runs) were performed. The convergence of the chains to their ergodic distribution was tested via the Gelman-Rubin (GR) diagnostics integrated in OpenBUGS®. The significance of the parameters was tested with the step function implemented in OpenBUGS®. At each iteration for a variable X, step(X) equaled 1 if X ≥ 0 and equaled 0 if X < 0. At the end of the run, if P(X > 0) was lower than 0.1 or higher than 0.9, the parameter X was considered to be different from 0.

Results

Effect of the flood on the community of invertebrates

During the first trial (early spring), immediately after the artificial flood, Diptera, Ephemeroptera, Coleoptera, Plecoptera and the "Others" groups were less abundant in the impacted than in the control cages. The abundance of Mollusca and Crustacea increased, while the abundance of Trichoptera, Oligochaeta and the total abundance did not change (Figure 2 and Table 4 -α1). At the end of the trial, the abundance of Diptera, Ephemeroptera, Coleoptera, Oligochaeta and the total abundance decreased in the impacted cages, while the abundance of Crustacea increased. The abundance of Trichoptera, Mollusca, Plecoptera and the Other invertebrates remained equal between the impacted and the control cages (α2).

During the second trial (late spring), the artificial flood decreased the abundance of all groups of invertebrates (α1 and γ1). At the end of the trial, the abundance of Diptera, Ephemeroptera, Crustacea, Coleoptera, Oligochaeta and the total abundance remained lower in the impacted cages, while the abundances of Plecoptera and Exogenous were higher. The abundance of Mollusca, Trichoptera and the Other invertebrates remained equal between the control and the impacted cages (α2 and γ2).

Whatever the conditions, there was a significant seasonal effect comparing the two trials. The abundance of Diptera, Trichoptera, Ephemeroptera, Oligochaeta, Crustacea, Others and the total abundance was higher in late spring than in early spring (β1 and β2).

The important information to better understand trophic availability concerns the main prey groups for 0 + trout, i.e. Diptera and Ephemeroptera. For these groups (but still true for total abundances):

 Just after the flood and at the end of the trials, abundances in control were higher than in impacted cages in both early and late spring and these differences were more pronounced at the end of the trial than just after the flood,  Abundances were much higher in late than in early spring for Diptera, which were ten times more abundant than Ephemeroptera. Table 4 Mean of the posterior probability distribution functions of the effect of the flood (α), season (β) and the interaction flood*season (γ) on the logarithm of the abundance of invertebrates. Effects were estimated for each group and for the total abundance, immediately after the flood and at the end of the trial. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

B

Behaviour of alevins

During the first trial (early spring), mortalities were recorded at the end of the 21 days of the observation survey, but we ignore when they arose. There was only 50% of survival with only two alevins surviving out on the four initially set up in each cage. Survivors grew, between 0.083 and 0.327 in the control and between 0.143 and 0.748 in the impacted cages (Table S2).

Alevins appeared to do more attempts to catch prey in the control cages and there were also more aggressive interactions. However, because the number of alevins in the cage changed over time, it was not possible to analyse behavioural data for the first trial.

During the late spring surveys, we found a strong common pattern in the distribution of the four alevins within each cage. The dominant fish was positioned in the upstream part of the cage, immediately behind the net through which the invertebrates penetrated by drift into the cage.

Then, the subdominant fish was positioned side by side or just downstream the dominant. The last two subordinates were generally positioned in the downstream part of the cage and accessed only to prey uncaught by the two upstream fish. The dominant and the subdominant fish gained the most weight and caught the most prey (Table 6), except in cage 2 in which differences in growth and feeding activity were less marked between alevins ranked B, C and D. There was no significant difference in average individual weight gain of alevins according to the conditions (P(WGFlood > WGControl) = 0.818).

Table 6 Initial and final weight, individual weight gain, mean number of feeding attempts by survey, One-Side-Attacks made (OSA+), sustained (OSA-), social rank and positioning of the alevins during the second trial (late spring) according to their cage of origin and the colour of their tag. Over the 15 days of observation, the probability of activity, the number of attempts to catch prey and the number of aggressive interactions during a survey (five minutes) increased but the intensity of the increase always differed between the control and the impacted cages (Table 7).

Table 7 Mean of the posterior probability distribution functions of the increase in the activity probability or in the occurrence of discrete events (i.e. attempts to catch prey or aggressive interactions) over time (δ1 being the increase in control and δ2 in impacted cages) and the effect of cage positioning (λ). Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

Behaviours

Activity

Activity increased with time in both control and flood cages. Under impacted conditions, probabilities of being active were slightly higher (Figure 4).

Figure 4 Model estimations of the probability to be active of an alevins according to time (in days) in the control (in blue) or in the impacted conditions (in red). Shaded areas are the 95% probability intervals of posterior distributions.

Feeding attempts

Number of feeding attempts increased with time in both conditions, but the increase was more pronounced under control conditions from day 3-4. Fish made on average 6 feeding attempts by survey on the first day, 11 vs 10 on the seventh day and 21 vs 18 on the fifteenth day (Figure 5).

Capture distance

Alevins mostly sought prey around them (0-2 cm, 50.8% of total number of captures) but they also travelled intermediate distances (2-5 cm, 32.1%) or even longer (more than 5 cm, 17.1%).

However, there was no significant difference in the temporal trend of the proportions of distances travelled by alevins between control and impacted conditions (Figure 6). It can only be noticed that short distance captures represented 80-90 % of the catches during the first 2 days in the control cages, whilst it established around 60% at the same time in the impacted cages.

Figure 6 Proportion of short (< 2 cmin light grey), medium (2-5 cmin grey) and long (> 5 cmdark grey) feeding attempts of alevins according to time (in days) in control and in impacted conditions.

Number of aggressive interactions

Aggressiveness increased with time in both conditions, but the increase was more pronounced under control conditions from the sixth day. Alevins made no attack on the first day, 1.5 vs 1 attack on the seventh day and 6.5 vs 2.5 attacks on the fifteenth day (Figure 7).

Relationship between behaviours

Individual weight gain was correlated with the activity of alevins as well as the average number of attempts to capture prey by survey. Activity of alevins seemed to be correlated with the Flood average numbers of attempts to capture prey by survey. In all three cases, the fit was higher than 0.50 and slope coefficients (a) were significantly positive (Table 8). The individuals which gained the most weight were the most active and they hunted the most (Figure 8). On the contrary, no link was established with aggressiveness.

Table 8 Correlations between individual weight gain (WG) and the number of survey noted active by alevins, the average number of attempts to catch prey by alevin and the average number of aggressive interactions by alevin. Activity of alevins was also related to the number of attempts to catch prey and the average aggressive interactions. Mean of posterior probability distribution functions of a (the slope coefficient) and b (the intercept) and fit of the models. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold). Table 9 Synthesis of the main results on the effects of the artificial flood on invertebrate abundances (total and Diptera), alevin performances (survival and growth) and alevin behaviour (activity, feeding activity and competition) in early and late spring.

Discussion

The experiment conducted in the semi-natural channel of the Lapitxuri highlighted that the artificial flood significantly impact the invertebrate community and the alevin performances and behaviour. However, the impact of the flood differed according to season.

Artificial flood and invertebrate abundances

In early spring, the artificial flood reduced the abundances of Diptera and Ephemeroptera to a third, while the total abundance of invertebrates remained more or less equal. In late spring, the artificial flood decreased by half the abundances of Diptera, Ephemeroptera and the total abundance. It is possible that the low abundances in early spring limited the impact of the artificial flood as observed in other streams when floods arose when invertebrate abundances were low [START_REF] Arunachalam | Substrate selection and seasonal variation in densities of invertebrates in stream pools of a tropical river[END_REF][START_REF] Brewin | The seasonal dynamics and persistence of stream macroinvertebrates in Nepal: do monsoon floods represent disturbance?[END_REF]. After twenty days, differences persisted. At that time abundances were the result of both recolonization processes and fish predation [START_REF] Grosholz | The influence of flood cycle and fish predation on invertebrate production on a restored California floodplain[END_REF]. Focusing on Diptera and Ephemeroptera, abundances remain lower in the impacted than in the control cages. However, the catch up was much quicker in late spring than in early spring. It could be due to an increase in system productivity that promotes the recolonization process as observed by [START_REF] Miller | Effects of spates and drying on macroinvertebrate assemblages of an intermittent and a perennial prairie stream[END_REF].

Artificial flood and performances of alevins

In early spring, the artificial flood diminished survival in large cages by 23% when compared to control. According to density-dependence mechanism this should reduce competition and promote growth. The growth of alevins was significantly but only slightly lower in the impacted cages. Therefore, the impact of trophic resources has probably been high enough to counterbalance the positive effect of density-dependence. Another possible explanation is that mortalities occurred only shortly before the end of the experiment, before growth compensation.

Then, the occurrence of a flood when the abundance of invertebrates is already low can seriously weaken the strength of the cohort. In late spring, the artificial flood increased unexpectedly the survival by 13%, while it diminished the individual weight gain. Density was higher in the impacted cages and then, the density-dependence mechanisms could partly explain the lower growth. Food availability probably might have had an effect also, since Diptera and Ephemeroptera were more abundant in control than in impacted conditions. It remains difficult to evaluate the respective impact of density and trophic resource limitation on alevin growth (see Supplementary Information Table S3, S4 and Figure S2 for details). The higher survival in impacted cages is surprising and it was probably linked to uncontrolled factors such as habitat availability. Indeed, it remains possible that the artificial flood, by washing fine sediment in between the gravels, favoured the visual isolation between 0 + fish since they are very small during their first weeks of growth (23 mm long and 0.1 g on average for a first feeding alevins)

and visual isolation is a well know factor that limits the strength of the competition in salmonids [START_REF] Huntingford | Social status and feeding in Atlantic salmon Salmo salar parr: the effect of visual exposure to a dominant[END_REF][START_REF] Imre | The effect of visual isolation on territory size and population density of juvenile rainbow trout (Oncorhynchus mykiss)[END_REF].

Artificial flood and behaviour of alevins

Alevins were seen swimming actively more often in the cages impacted by the artificial flood than in the control ones. This is consistent with [START_REF] Biro | Density-dependent mortality is mediated by foraging activity for prey fish in whole-lake experiments[END_REF] who highlighted that in low food conditions, young trout (Oncorhynchus mykiss) needed to increase their activity to maintain a constant supply of food and then, grow. Present results confirm that when resource is scarce, young salmonids exhibit more risky behaviour, increasing their foraging effort and probably increasing the predation risk in the same time.

Our behavioural observations were in accordance with current knowledge: social status matched with the number of feeding attempts and with individual weight gain. The dominant fish was the largest at the beginning of the experiment in only one of the four cages. It suggests that the body condition of fish (weight and/or size) is rather the consequence of dominance than the cause as suggested by [START_REF] Metcalfe | Intraspecific variation in competitive ability and food intake in salmonids: consequences for energy budgets and growth rates[END_REF]. In the behaviour cages, the average individual weight gain did not differ between control and impacted cages, but inter-individual differences were higher in the impacted cages. At low food abundance, dominants monopolized most of the resource leaving a limited access to food for subordinates [START_REF] Maclean | Social status, access to food, and compensatory growth in juvenile Atlantic salmon[END_REF][START_REF] Höjesjö | Can laboratory studies on dominance predict fitness of young brown trout in the wild?[END_REF]. The relationship between fish aggressiveness and food abundance is not yet clear. It has previously been argued that the strength of territorial defence depended on resource availability, with highest levels of territorial defence observed at intermediate levels of food [START_REF] Toobaie | Effect of food abundance on aggressiveness and territory size of juvenile rainbow trout, Oncorhynchus mykiss[END_REF]. Accordingly, when food is scarce or abundant, the energetic cost of aggressiveness is greater than the benefit. However, results diverge: Symons (1968) and [START_REF] Slaney | Effects of prey abundance on density and territorial behavior of young rainbow trout (Salmo gairdneri) in laboratory stream channels[END_REF] found that frequency of aggressive encounter was greater at low prey level and during starvation. On the contrary, van Leeuwen et al. (2016) showed that the number of aggressions decreased at low food levels. Subordinates adopted an "energy minimizing" strategy (Johnsson et al. 1996), whereby they did not try to have a good feeding position and they avoided the energetic costs of both swimming against the water flow and potential battles over territories (van Leeuwen et al. 2016). Because investment in territorial defence can be costly for growth, [START_REF] Metcalfe | Intraspecific variation in competitive ability and food intake in salmonids: consequences for energy budgets and growth rates[END_REF] emphasized that the optimum strategy adopted by subordinate was minimizing energetic expenditures rather than maximizing food intake. This way, subordinates may continue to grow despite receiving a low level of food intake. This hypothesis is clearly strengthened by our results.

Artificial flood and season

In early spring, survival of alevins remained low (50% on average) and they did not gain weight.

The same trends was recorded in the observation cages, at least concerning survival. It seems that the low invertebrate abundances impacted significantly the performances of alevins and cause their death. In contrast, in late spring, survival was quite high (90% on average) and their growth was positive, even if much lower in impacted than in control cages. Early emerging fish have less competition pressure to access to the best feeding spots and they can grow efficiently as far as the productivity of the system is not a limiting factor. When late alevins emerge, early survivors are favoured by their residence anteriority on territories (Huntingford & Garcia de Leaniz 1997;[START_REF] Cutts | Prior residence, territory quality and life-history strategies in juvenile Atlantic salmon (Salmo salar L.)[END_REF]Harwood et al. 2003). However, the results from the present experiment suggest that if early emerging fish suffer from food shortage, they would undergo high mortality rates as well as negative growth.

Conclusion

Throughout the spring season, temperature as well as sunshine duration and brightness increase and boost the primary production. Correlatively, the invertebrate production increased, the recolonization process was more effective and it partially compensated the flood effects.

Therefore, the impact of a scouring flood on invertebrates might depend to a great extent on its timing (Robinson et al. 2004). That timing has to be taken into account when assessing the impact of flood on 0 + trout since consequences on survival and growth were not of the same magnitude (harsh in early spring, light in late spring). The previous experiment emphasised that hydrological disturbances decreased the food availability for fish but the consequences at the fish level depended on the productivity of the system. These results stemmed out from an experiment at high fish density (32 fish m -2 ) and one may wonder on the persistence of these effects on the fish population at low alevins density.

According to literature, mortality rate at low density is mainly density-independent while density-dependent growth still occurs [START_REF] Jenkins | Effects of population density on individual growth of brown trout in streams[END_REF]Grant & Imre 2005;[START_REF] Imre | Density-dependent growth of young-of-theyear Atlantic salmon Salmo salar in Catamaran Brook, New Brunswick[END_REF].

A protocol was set up in the experimental channel of the Lapitxuri in late spring to look at the performances of alevins at low density. A flood was simulated in four half-reaches, while four others were not impacted and used as control. Immediately after the flood, invertebrates were sampled and just-emerged brown trout alevins were introduced in all reaches at low fish density

(2.6 fish m -2 ). After 33 days, the surviving alevins were recovered and invertebrates were sampled. We aimed to test the following hypotheses:

1. Flood should diminish the abundance of invertebrates, 2. Flood should diminish growth of alevins, but not survival, 3. A relationship between the abundance of invertebrates and growth is awaited.

Material and Methods

The experimental channel of the Lapitxuri and experimental design

The experiment was conducted from April to the end of May 2015 in an experimental channel fed by the Lapitxuri brook, a tributary of the Nivelle river in south-western France (43°16' N, 1°28' W). Four reaches (10 m long and 2.8 m wide) were used for the experiment, two located at 30 meters and two at 80 meters downstream the flow entry (Figure 4.1). Each reach was divided longitudinally by a tarpaulin wall in two half-reaches of 14 m² (10 m long and 1.4 m wide). Upstream and downstream movements of fish were prevented by nets, and traps at the end of downstream nets allowed to catch drifting fish. Aerial nets protected fish from avian predation. Gravel bed was constituted by cobbles and pebbles, substrate particles ranging approximately from 1 to 5 cm. The average mid-water velocity was maintained during the whole experiment at 12.4 cm s -1 (SD = 3.9calculation based on the absolute value of velocity measurements) and the mean water temperature during the experiment was 13.34°C (SD = 1.04). Abiotic conditions in the channel were close to optimal environment for young trout in terms of substratum quality, water quality and temperature values [START_REF] Roussel | Diel and seasonal patterns of habitat use by fish in a natural salmonid brook: an approach to the functional role of the riffle-pool sequence[END_REF], characterize the biometry of the batch. Then, remaining individuals were randomly split into eight groups of 37 alevins and released in the eight experimental reaches of the Lapitxuri seminatural stream. The fish density in each reach was of 2.6 fish m -² (~8% of the density used for the previous experiment). Downstream traps were checked every morning, and after counting, fish were immediately released back into the reach they came from. After 33 days of growth, surviving alevins were recovered by successive removal of electrofishing (19-22 May). They were weighted, measured and subjected to a benzocaine lethal anesthesia. Eight alevins of each reach were randomly selected and all invertebrates contained in their stomachs were identified to family level when possible and counted. Then a total of sixty-four gut contents were analysed.

Benthic invertebrate sampling

Immediately after the flood simulation (15/04/2015) and at the end of the trial (19/05/2015), 3 invertebrate samples by reach were randomly collected using a Surber net (30 cm x 30 cm, 500 µm mesh). Samples were preserved in 70% ethanol. Invertebrates were identified and assigned to different groups: Diptera, Trichoptera, Ephemeroptera, Mollusca, Coleoptera, Plecoptera, Oligochaeta, Crustacea and Exogenous.

Statistical analyses

Abundance of invertebrates as well as the logit of the alevins survival probability, alevins individual weight gain and fish productivity (sum of final weights) were analysed using a bayesian modelling approach computed with OpenBUGS®. All the models were built with the same logic: we assumed that the variables of interest followed a normal distribution. Then, the mean of the variable (μ.Variable) was modelled according to two parameters: a fixed-effect relative to the flood (α) and a random effect relative to each j reach (βj), which integrated all the potential sources of variation other than the flood. The number of prey ingested by alevin followed a Poisson law and the mean (μ.Prey) was modelled according to these same two effects (α and βj).

Abundance of invertebrates

If Abondi is the number of invertebrates counted by sample, we assume:

𝜇. log(𝐴𝑏𝑜𝑛𝑑 𝑖 + 1) 𝑖 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑖 + 𝛽 𝑗 [𝑖] Where μ is the average of the logarithm of the invertebrate abundance, α is a fixed-effect parameter for the impact of the flood and β is a random effect corresponding to each j reach.

The same modelling treatment was applied to the total abundance of invertebrates as well as to the abundance of each group identified (i.e. Diptera, Trichoptera, Ephemeroptera, Mollusca, Coleoptera, Plecoptera, Oligochaeta and Crustacea) and for exogenous individuals.

Survival probability of alevins

For each j reach, number of survivors Sj is estimated from the number of fish caught at each electrofishing passage (C1j, C2j, C3j and C4j), the number of remaining fish after each passage (R1j, R2j and R3j) and the fish efficiency (p.Fj): Where μ is the average of the logit survival probability, α is a fixed-effect parameter for the impact of the flood and β is a random effect corresponding to each j reach.

𝐶1 𝑗 ~

Individual weight gain of alevins

The initial weight (IW) of the 40 alevins sub-sampled at the beginning of the experiment allowed to estimate the mean (μ.IW) and the standard deviation (σ.IW) of the alevins initial weight distribution. For each k survivor recovered at the end, its initial weight (W.Begk) was drawn in the alevin initial weight distribution (according to μ.IW and σ.IW), while its final weight was known (W.Endk). By j reach, the standard deviation of the final weight of alevins (σ.W.Endj) was also modelled to test if the flood affected the variability of alevins final weight.

Individual weight gain (WGk) was obtained by dividing the difference between the final weight and the initial weight by the initial weight. Then, we assume:

𝜇. 𝑊𝐺 𝑘 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑘 + 𝛽 𝑗[𝑘]
Where μ is the average weight gain of alevins, α is a fixed-effect parameter for the impact of the flood and β is a random effect corresponding to each j reach.

Total number of prey ingested by alevins and the number of individuals of Chironomidae and Baetidae families (which are the main food items just-emerged alevins) were analysed. Then, if Preyl is the number of prey ingested by the l th alevins, we assume:

𝑃𝑟𝑒𝑦 𝑙 ~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛(𝜇. 𝑃𝑟𝑒𝑦 𝑙 ) log (𝜇. 𝑃𝑟𝑒𝑦 𝑙 ) = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑗 + 𝛽 𝑗
Where μ is the average number of prey ingested, α is a fixed-effect parameter for the impact of the flood and β is a random effect corresponding to each j reach.

Bayesian computations

Parameters were given independent "weakly informative" priors. Following recommendations of [START_REF] Gelman | Data analysis using regression and multilevelhierarchical models[END_REF], μ, α, μ.IW, σ.IW, a and b were sampled in a normal distribution, N(0, 100). βj were drawn in a normal distribution, N(0, σβ²), with σβ sampled in a truncated half-Cauchy distribution, t(0, 1, 1). p.Fj were sampled in a beta distribution, beta(1, 1).

Hyperparameters of σ.W.Endj (B.σ.W.End and E.σ.W.End) were sampled in a gamma distribution, gamma(1, 1). We used each time three independent chains, the first 10 000 iterations were discarded as an initial burn-in period. Then, 10 000 further iterations (resulting from one every ten runs) were performed. The convergence of the chains to their ergodic distribution was tested via the Gelman-Rubin (GR) diagnostics integrated in OpenBUGS®. The significance of the parameters was tested with the step function implemented in OpenBUGS®.

At each iteration for a variable X, step(X) equaled 1 if X ≥ 0 and equaled 0 if X < 0. At the end of the run, if P(X > 0) was lower than 0.1 or higher than 0.9, the parameter X was considered to be different from 0.

Results

Effect of the flood on the community of invertebrates

Immediately after the artificial flood, most invertebrate groups and the total abundance of invertebrates were negatively impacted (except Mollusca -Figure 4.2). However, this tendency was never significant (except for Coleoptera (P(α1 > 0) = 0.054, Table 4.1). At the end of the trial, all groups (except Diptera) were still less abundant in the impacted reaches, but again differences were not significant, (except for Ephemeroptera and Mollusca (P(α2 > 0) = 0.037 and 0.052 respectively). Survival probability reached 84% in the control and only 63% in the impacted reaches (Figure 4.3). At the beginning of the experiment, the batch of 40 alevins weighted on average 0.104 g and the SD was low (0.004). At the end of the trial (33 days of growth), fish were lighter in the control (0.459 g on average), than in the impacted reaches (0.505 g). Accordingly, weight gain was lower in the control (3.411) than in the impacted reaches (3.854). In addition, alevins weights were more homogenous in the impacted reaches (SDF1 = 0.060, SDF2 = 0.065, SDF3 = 0.087 and SDF4 = 0.090), than in the control ones (SDC1 = 0.098, SDC2 = 0.104, SDC3 = 0.092 and SDC4 = 0.113). Finally, fish productivity reached on average 13.23 g in control reaches and only 11.31 g in the impacted ones.

To sum up, alevin survival probability and fish productivity were significantly lower in the impacted reaches than in the control ones (P(α > 0) = 0.012 and 0.084, respectively -Table 4.2), while individual weight gain was higher (P(α > 0) = 0.907).

Relationship between the community of invertebrate and alevins

Alevins performances (number of survivors, average individual weight gain, fish productivity)

were related to the abundances of each invertebrate group (10 groups: Diptera, Trichoptera, Ephemeroptera, Mollusca, Coleoptera, Plecoptera, Oligochaeta, Crustacea, Exogenous and Total) both immediately after the flood (Table 4.3) and at the end of the trial (Table 4.4), then 60 relationships were tested. On the 60 relationships tested, 45 fits of correlations were negative and 15 fits were positive but lower than 0.50. This meant that the link between the variables tested was low or inexistent. Focusing on Diptera and Ephemeroptera orders, despite the poor

Discussion

We failed at pointing out a significant effect of the artificial flood on the community of invertebrates. There was a high variability among the 3 samples characterizing a given reach, and this is probably why results were not significant. Thus, despite the homogeneity of the reaches in terms of water velocity, depth and gravel size, this suggests a huge heterogeneity in * * spatial distribution of invertebrates as observed in the field [START_REF] Downes | Habitat structure and invertebrate assemblages on stream stones: a multivariate view from the riffle[END_REF][START_REF] Heino | Identifying the scales of variability in stream macroinvertebrate abundance, functional composition and assemblage structure[END_REF]). Nevertheless, the repeated trend for every taxa was a decrease in abundance in the impacted reaches and this trend persisted thirty-three days after the flood (except concerning Diptera). Because Diptera are essential in fish diet (see gut contents results), they deserve a specific attention. In control reaches, Diptera abundances double during the May month, probably in accompaniment to the increase in primary productivity and temperature. This happened also in the impacted reach, the average effect was much higher (more than 10 times increase), even if quite heterogeneous among reaches. All groups of invertebrates showed the same trend but Diptera was the only group exhibiting such a high increase in the impacted reaches.

As there was no significant impact of the artificial flood immediately afterwards, it was not expected to detect differences at the end of the trial, thirty-three days later. However, there were significantly less Ephemeroptera and Mollusca in the impacted reaches. Although there was no significant difference immediately after the disturbance, enough adults could have been displaced and/or the clutches hooked to the gravel [START_REF] Gaino | Synthesis and function of the fibrous layers covering the eggs of Siphlonurus lacustris (Ephemeroptera, Siphlonuridae)[END_REF]Tachet et al. 2010) could have been decimated, weakening the next generation of these two orders.

Surviving alevins were more homogenous in weight in the impacted reaches than in the control ones. It is likely that the lack of food affected first the weaker ones, which would die because of their inability to establish a feeding territory and a lower amount of reserves than bigger alevins. This hypothesis is supported by [START_REF] Good | Annual variation in sizeselective mortality of Atlantic salmon (Salmo salar) fry[END_REF] who concluded that early mortality associated with hydroclimatic events was high for small Atlantic salmon as they were not strong enough to survive. Then, massive mortalities caused by hydrological events reduce variability in individual growth rate (Vincenzi et al. 2012). The higher weight gain of alevins in impacted reaches than in the control ones could be related to density-dependent mechanisms with few survivors sharing the same amount of resources. Another explanation may rely on the difference in habitat availability. Indeed, floods mobilise fine particles filling the interstices of the gravel bed [START_REF] Kondolf | The flushing flow problem: defining and evaluating objectives[END_REF][START_REF] Mürle | Effects of experimental flooding on riverine morphology, structure and riparian vegetation: The River Spöl, Swiss National Park[END_REF][START_REF] Lepori | Disturbance and aquatic biodiversity: reconciling contrasting views[END_REF], which maintain the heterogeneity of the substrate and provide shelters. Habitat heterogeneity increases the visual isolation between alevins and reduces the strength of the aggressive interactions (Kalleberg 1958;[START_REF] Bolliet | Does embeddedness affect growth performance in juvenile salmonids? An experimental study in brown trout, Salmo trutta[END_REF]. The combined effect of a diminution in the numbers of competitors and a heterogeneous habitat may have enhanced fish growth [START_REF] Letcher | Maturation of male age-0 Atlantic salmon following a massive, localized flood[END_REF]. Gut content analyses highlighted a higher number of prey ingested by alevins in the impacted than in the control reaches and a large amount of these prey were Chironomidae. [START_REF] Cross | Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon[END_REF] found that a flood in the Colorado River (USA) decreased the number of benthic invertebrates while the production of rainbow trout (Oncorhynchus mykiss) increased. The trout production was mainly supported by invertebrates from the Simuliidae and the Chironomidae families, which both represented 50% of the prey ingested. They suggested that the flood created favourable conditions for age-0 trout: the scouring of the benthos associated with the flood enhanced the quality of the habitat and increased the production of Simuliidae and Chironomidae, which need a clean substrate for attachment. These taxa are also the pioneers at the recolonization time after hydrological disturbances (Otermin et al. 1998). Despite the absence of significant difference in the invertebrate community, it can be argued that the Chironomidae preferentially colonized the impacted reaches because the habitat was of better quality but the intrinsic variability of the experimental channel masked this phenomenon.

Therefore, the increase in Diptera in the impacted reach may have also favour growth. However, the highest growth of alevins did not compensate for their lower survival and fish productivity was reduced by the impacted reach.

We predicted that reaches with a good food availability (high abundance of invertebrates)

would enable the settlement of a stronger fish population (high numbers of survivors, high average weight gain and high productivity) but it was difficult to put forward this kind of relationship. Although abiotic factors were controlled and the habitat simplified compared to natural environment, the invertebrate community was unevenly spread and the heterogeneity in the invertebrate distribution limited the information provided by benthic samples. Additionally, [START_REF] Weber | Accounting for spatial and temporal variation in macroinvertebrate community abundances when measuring the food supply of stream salmonids[END_REF] demonstrated that measuring the biomass of invertebrates drifting was more accurate to estimate food supply for fish. Even if there is a link between benthic invertebrates and drifting invertebrates, direct relationship between benthic community composition and the productivity of salmonids is not well established (Faush et al. 1988). Moreover, sampling at a given time does not reflect the dynamics of the invertebrate population and the food availability on the 33 days that lasted the experiment. More accurate analysis taking into account only small larvae of Chironomidae, Simuliidae and Baetidae might improve our understanding of the mechanisms at work.

Conclusion

The impact of the artificial flood was low on the invertebrate assemblage, while it was obvious on brown trout young stages. At low fish density (2.6 fish m -2 ), the artificial flood decreased fish survival and improved fish growth (probably in relation with a mix between habitat, density and food availability). Despite our inability to identify the proximal factors underpinning fish performances, emergence appears to be a critical stage, highly sensitive to hydrological disturbances since even at low density, and with relatively low impact on benthic invertebrates, fish productivity was negatively impacted.

In the following section, I will highlight how results from different chapters complement each other to answer the original question: what could be the consequences of Global Climate Change (GCC, and notably an increase in temperature and flood frequency/intensity) on the trophic availability in invertebrate larvae for young trout stages. Then, the discussion will go through a more speculative part on the potential evolutionary consequences of GCC on salmonid phenology, before concluding on some perspectives.

Studying the ecological consequences of a flood by sampling in the wild is a tricky challenge.

Indeed, because of the unpredictability of floods it is highly hazardous to gather enough points to assess the effects of flood on young emerging salmonids within a 3-year study. In addition, the instream habitat heterogeneity can mask or modify the effect of a disturbance. For all these reasons, we worked in a semi-natural environment. These experiments allow to evaluate the consequences of a flood in a context where invertebrate production and renewal is very close to the wild. The simulation of the flood with a motor pump created a flush that disturbed the gravel like a scouring flood. However, the impact of the simulated flush was restricted both in space and time. In addition, abiotic factors characterizing natural floods such as the gradual increase in water level and in turbidity were not reproduced. These cues annunciate the flood to the biota, which can shelter [START_REF] Doeg | Effect of experimentally increasing concentration of suspended sediment on macroinvertebrate drift[END_REF][START_REF] Suren | Effects of deposited sediment on invertebrate drift: an experimental study[END_REF]. However, in the case of a scouring flood, the substratum is mobilised and most of the invertebrates are dislodged, including the sheltered ones. Water velocities obtained by the use of the motor pump were probably of the same order than velocities observed during natural scouring floods.

Invertebrate species responded quite differently to the flood disturbance, but at least for Diptera and Ephemeroptera, densities were reduced.

In the chapters 3 and 4, we conducted experiments at two fish contrasted densities. In nursery streams, clutches are aggregated and it can result in high fish densities on the spawning grounds [START_REF] Elliott | The critical-period concept for juvenile survival and its relevance for population regulation in young sea trout, Salmo trutta[END_REF]. We worked at 32 and 2.6 fish m -2 and this last value corresponds to the carrying capacity in 0 + trout of the Lapitxuri semi-natural stream after emigration post-emergence (A. Bardonnet, pers. com.). Analyses of alevin performances in late spring were quite disrupting between high (cages experiment) and low (reach experiment) density as the impacts of the flood diverged. At low density, survival was lower and growth higher in the impacted condition when compared to control, while the opposite was observed at high density. In addition, survival rates were quite similar according to density in the control conditions (0.84 at low density vs 0.81 at high density) but survival was much lower at low density in flooded conditions (0.63 at low density vs 0.94 at high density). Looking at weight gain, growth performances were 5 times higher at low density in control, and even more in the impacted conditions. One possible explanation of such pattern may lie in the rhythm of the invertebrate recolonization process.

Invertebrates sampling differed between low and high density protocol, preventing the comparison of abundances. However, we can compare differences in the pattern of abundances between the start and the end of the experiment. At high fish density, the flood diminished the abundances of Diptera immediately after the disturbance and one month later, Diptera did not recolonize and their abundance keep on declining, while the opposite was observed at low fish density. This was probably due to a high fish predation rate decreasing the Diptera abundances in the cages. In the reaches, fish density was too low to affect Diptera abundances and allowed the course of the recolonization process. We can then hypothesize that at the start of the experiment, the abundances of good sized preys, mainly chironomids, in the cage (high density)

and in the reach (low density) were similarly low. However, prey were easy to catch in the cages because water velocity was lower, hunting surface was restricted and the prey shelters was limited to a small gravel layer. Therefore, alevins in the reaches were more likely to face a period of starvation leading to mortality than in the cages. Later, Diptera dramatically decreased in the cages, limiting fish growth. In the reach, the recolonization process was very efficient leading to the replenishment of Diptera and to excellent growth performances (confirmed by the number of chironomids in the gut content).

Of course, this scenario is speculative and many uncontrolled factors might have shape our results. Among them, temperature was on average one degree higher during the low density experiment and it may have interact. Water temperature averaged 10.2, 12.3°C at high density (chapter 3) and 13.3°C at low density (chapter 4). The last value is close to the upper thermal tolerance of alevins (Ojanguren & Braña 2003;[START_REF] Lahnsteiner | Thermotolerance of brown trout, Salmo trutta, gametes and embryos to increased water temperatures[END_REF], and it may have increased the mortality rate of starving fish (as observed in chapter 2). Temperature may also interfere with growth. However in chapter 2 we observed no difference in growth between 8 and 11°C

for fish fed ad libitum (despite the fact that they almost double the food intake at warm temperature). In addition, after a delayed access to food, alevins were more capable to resume a normal diet and growth was higher at low temperature (12% differences in growth catch-up between the two temperatures). This was quite surprising and could be due to the short duration of the experiment, but it could also be the consequence of a growth optimum closer to 8 than to 11°C in trout early ontogenesis [START_REF] Elliott | Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: predicting the effects of climate change[END_REF].

Fish growth and survival are linked in a complex and hardly predictable manner and the intensity of the density-dependent mechanisms partially enlighten the results of the Lapitxuri experiments. At high population abundance, space is limited and density-dependent mortality and emigration occur. At low population abundance, mortality rate is mainly density independent while density-dependence only operates on growth during the earliest time period [START_REF] Jenkins | Effects of population density on individual growth of brown trout in streams[END_REF]Grant & Imre 2005;[START_REF] Imre | Density-dependent growth of young-of-theyear Atlantic salmon Salmo salar in Catamaran Brook, New Brunswick[END_REF][START_REF] Lobón-Cerviá | Density-dependent growth in stream-living Brown Trout Salmo trutta L[END_REF]. It was only during a second time period that density-dependence operates on both growth and mortality.

This way, the population can persist after catastrophic disturbances and recover quickly, which reduces the extinction risk of stream dwelling salmonid populations in variable environments.

Vincenzi et al. ( 2008) pointed out that the population abundance of marble trout (Salmo marmoratus) was diminished by severe floods, while growth of surviving juveniles was stimulated. This phenomenon was linked to both high food supply and low competition due to a low population density following the event. Moreover, females were larger to sexual maturity and produced more eggs. The increase in fertility allowed the population to recover fast and to be highly resilient.

Throughout spring, temperature, sunshine and brightness duration increase and initiate both primary and secondary production. Consequently, stream productivity is higher in late spring than in early spring. Emergence is spread during spring and according to literature (e.g. [START_REF] Einum | Selection against late emergence and small offspring in Atlantic salmon (Salmo salar)[END_REF], the timing of salmonid emergence is under high selective pressure. The timing of emergence is the result of a trade-off between food and territory availabilities [START_REF] Bromage | The environmental regulation of maturation in farmed finfish with special reference to the role of photoperiod and melatonin[END_REF][START_REF] Letcher | Long-term consequences of variation in timing and manner of fry introduction on juvenile Atlantic salmon (Salmo salar) growth, survival, and life-history expression[END_REF] -Figure 1). Alevins emerging in early spring undergo harsh environmental conditions with a reduced amount of food. However, the low density of alevins allows them to establish feeding territories on the best hunting spots, giving them a growth advantage. In contrast, alevins emerging in late spring face good environmental conditions with an abundant amount of food but the number of territories available in optimal habitat is limited and intensifies the strength of the competition between congeners. The prior residency of the early emergent alevins gives them a significant advantage (Johnsson et al. 1999;Harwood et al. 2003). These mechanisms maintain a variability in the timing of emergence but this could be disrupted by Global Climate Change. Currently, prey availability matches with the timing of emergence. GCC should increase the intensity and the frequency of stochastic hydrologic events such as catastrophic floods. This could directly diminished the production, the abundances of stream invertebrates by displacing and killing them and consequently, reduced the prey availability for carnivorous fish.

Additionally, GCC should increase the temperature and affect the phenology of brown trout, including the timing of emergence. As brown trout is an ectotherm, warmer temperature will reduce the development duration and advance the timing of emergence. Consequently, GCC should delayed the peak of prey availability while the emergence date should be advanced (Figure 2). Nevertheless, temperature affects the whole ecosystem metabolism. According to [START_REF] Woodward | Climate change and freshwater ecosystems: impacts across multiple levels of organization[END_REF], warmer temperature should put forward the initiation of both primary and secondary productivity. Therefore, a move forward in the emergence date might be supported by an earlier primary production [START_REF] Demars | Temperature and the metabolic balance of streams[END_REF]) and a higher leaf decomposition rate [START_REF] Pereira | Spring stimulates leaf decomposition in moderately eutrophic streams[END_REF], leading to high invertebrate production. Invertebrate drift should also be enhanced: "mechanically" with the increase in invertebrate abundance, and also in relation to drifting behaviour. Results from chapter 1 suggested that Baetis drift may depend on temperature. This experiment was not designated to look at the effect of temperature, but differences in temperature (12.2 vs 14.7°C) accompanied the spring and autumn trials. In the autumn trial, the warmest, Baetis drift rate was increased by nearly a third (26%). The increase in temperature could then mitigate the negative effect of flood on invertebrates.

Temperature should also affect the proximal mechanisms affecting the timing of emergence (Figure 3). We know that an increase in temperature will at first reduce the time needed for the embryo-larval development as an immediate physiological response to temperature. This will lead to an earlier emergence date. The quicker development of the embryo-larval stages can be compensated by a delay in the spawning season, which depends strongly on the photoperiod and falling autumn temperature [START_REF] Beacham | Temperature, egg size, and development of embryos and alevins of five species of Pacific salmon: a comparative analysis[END_REF][START_REF] Pankhurst | Effects of stress on reproduction and growth of fish[END_REF][START_REF] Pankhurst | Cold and dark or warm and light: variations on the theme of environmental control of reproduction[END_REF][START_REF] Pankhurst | Temperature and salmonid reproduction: implications for aquaculture[END_REF][START_REF] Pankhurst | Effects of climate change on fish reproduction and early life history stages[END_REF]. Literature suggests that genitors adjust the spawning date to match the emergence with favourable conditions of water flow, temperature and food supply [START_REF] Crozier | Potential responses to climate change in organisms with complex life histories: evolution and plasticity in Pacific salmon[END_REF]. Hence, Unwin et al. (2000) demonstrated that later spawning occurs in chinook salmon populations where embryos develop in warmer water. Similarly, [START_REF] Webb | Variation in the time of spawning of Atlantic salmon (Salmo salar) and its relationship to temperature in the Aberdeenshire Dee, Scotland[END_REF] (2002) demonstrated that warmer temperature increased the egg size but this affects the survival of eggs. Large eggs are stenotherm (Régnier et al. 2013). Moreover, maximal egg size is related to the female size [START_REF] Hendry | Revisiting the positive correlation between female size and egg size[END_REF] and maternal fitness is a trade-off between egg size and egg number [START_REF] Einum | Egg-size evolution in aquatic environments: does oxygen availability constrain size[END_REF]. Then, warmer temperature should increase the mortality rate of large eggs and the egg size increase should decrease the egg number. The pre-emergence mortality should be higher, resulting in fewer emerging fish. However, large eggs develop faster and emerge early [START_REF] Einum | Egg-size evolution in aquatic environments: does oxygen availability constrain size[END_REF][START_REF] Rollinson | Why does egg size increase with maternal size? Effects of egg size and egg density on offspring phenotypes in Atlantic salmon (Salmo salar)[END_REF]. Early emerged alevins have higher metabolic rates (Régnier et al. 2012a) and the energy conversion efficiency into growth is higher for large eggs than for small eggs (Régnier et al 2012b), which produces larger alevins.

These four factors should support post-emergence survival and confer a competitive advantage to alevins hatching from large eggs but it is difficult to evaluate if the strategy to produce large eggs should be favoured by GCC. GCC should also be a selective force by selecting individuals according to their temperaments or personalities, which induce individual differences in exploration and activity (Le [START_REF] Galliard | Personality and the pace-oflife syndrome: variation and selection on exploration, metabolism and locomotor performances[END_REF]. These behaviours are associated with foraging, territorial defence, mate finding, and dispersal as well as habitat choice. For example, individuals with high activity increase both growth and the risk of mortality from predation, thus resulting in a similar fitness than low activity individuals (Stamps 2007). [START_REF] Réale | Personality and the emergence of the pace-of-life syndrome concept at the population level[END_REF] associated behaviour (i.e. activity, superficial exploration, boldness and aggressiveness) with physiology (i.e. immunity, metabolism and oxidative stress). This so-called "pace-of-life" syndrome emphasizes integration of behavioural variation within a slow-fast demographic and metabolic continuum.

If we try to evaluate the impacts of GCC in this theoretical context of "pace-of-life" syndrome, alevins should be selected according to their probability to avoid starvation or their ability to withstand it during period of food scarcity (Figure 4). With a high prey production in late spring, alevins emerging later diminish their risk of starvation. Likewise, alevins with low metabolic rate and a high amount of reserves have a high ability to survive during starvation. Late emerging alevins have low metabolic rates, then it is possible that alevins exhibit these two traits simultaneously as they are probably correlated but these alevins usually have a low amount of reserves. According to the "pace-of-life" syndrome [START_REF] Ricklefs | The physiology/life-history nexus[END_REF][START_REF] Réale | Personality and the emergence of the pace-of-life syndrome concept at the population level[END_REF], shy alevins which are associated with low metabolic rate could be favoured.

This selection could have consequences on adult spawning strategies, on maternal investment and on the whole population functioning and dynamics. Despite this very speculative scenario, it is still difficult to predict which direction will take the selection with Global Climate Change and, for now, very few elements are available.

Figure 4 According to the environmental conditions (food scarcity), alevins could be selected depending on their ability to avoid or to face periods of starvation. This could have consequences on life history strategies and on the whole population functioning.

We did not succeed in increasing sufficiently the water velocities in our experimental facilities to approach flood velocities. However, the link between moderate water velocities and the invertebrate propensity to drift has been clarified for the three species we focused on. Even if results depended in part on the experimental conditions, we found three distinct patterns and we provided a precise quantification of the drift probability of the taxa studied. Laboratory experiment also allowed us to assess the ability of alevins to face starvation, the consequences of such stress on their metabolism and their ability to recover according to temperature. These two experiments could be reproduced with temperatures ranging from 4 to 14°C, which corresponds to the limits of the thermal tolerance of brown trout alevins (Elliott 1994) and temperature that alevins can face throughout their distributional range [START_REF] Jonsson | A review of the likely effects of climate change on anadromous Atlantic salmon Salmo salar and brown trout Salmo trutta, with particular reference to water temperature and flow[END_REF]. Consequently, according to thermal conditions, the drift propensity of invertebrates would allow to evaluate the trophic availability for alevins (and more widely for drift-feeding fish) and the metabolic, survival and growth consequences of alevins facing periods of starvation would be nearly known. These data (those already acquired and those from future potential experiments) should be used as basis for calibrating and developing a global individual-based model [START_REF] Grimm | Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?[END_REF] to simulate scenarios of GCC and to better understand the consequences on brown trout alevin survival and growth [START_REF] Clark | Predicting climate change effects on appalachian trout: combining gis and individual-based modeling[END_REF]. Incrementing results from studies on the phenology and the productivity of systems could provide complementary elements for modelling climate change related scenarios and the consequences of rising temperature and occurrence of flood on a brown trout population.

It is forecasted that Global Climate Change should increase the intensity of hydrological events as well as their frequency. [START_REF] Connell | Diversity in tropical rain forests and coral reefs[END_REF] proposed the Intermediate Disturbance Hypothesis (IDH), which states that in absence of disturbance, species richness is expected to be low with more competitive species dominating and outcompeting the others. Similarly, when disturbances are too frequent, few species are able to resist to the changes in their environment or to colonize during the brief periods between disturbances. Under a disturbance regime that is intermediate in frequency and intensity, both rapid colonizers and more competitive species co-occur and species richness is maximised (Townsend et al. 1997). The increase in flood frequency associated with GCC should diminish species diversity of invertebrates and this could potentially affect the quality of prey for brown trout. Chironomids are pioneer species, have short life cycle and quick turn-over rates. As they constitute the main part of brown trout diet, frequent floods could increase the production of Chironomids and make them more available for brown trout.

Lastly, in Southern and Mediterranean Europe, GCC should induce more droughts in spring.

Droughts reduce the volume of water available for fish, impeding or preventing their migration and adversely affecting water quality, especially water temperature and dissolved oxygen [START_REF] Elliott | Pools as refugia for brown trout during two summer droughts: trout responses to thermal and oxygen stress[END_REF]. The aggregation of organisms (invertebrates and fish) could modify the intra-/inter-specific interactions, as well as the amount of prey and their availability for predators.

Then, it should be interesting to study the consequences of drought on the invertebrate assemblage and on just-emerged alevins.

To conclude, this work highlighted the possible consequences of GCC on the invertebrate fauna and on young trout stages of brown trout. In a local management context, it is difficult to mitigate the effects of GCC on riverine ecosystems. The building of a flood control dam would limit the intensity of floods but would not reduce their occurrence. It would be necessary to study the consequences of these facilities on the hydrological river regime and on the biota in order to evaluate their effectiveness. However, it appears necessary to limit other sources of disturbance to the biota (i.e. pollution or habitat fragmentation) in order to limit the stress induced by the interaction between GCC and others potential factors.

  Figure 1 Predictions from Dankers & Feyen (2008) about the consequences of Global Climate Change in the average seasonal precipitation in (a) winter, (b) spring, (c) summer and (d) autumn in Europe.

Figure 5

 5 Figure 5 Invertebrates & brown trout: a Match-Mismatch Hypothesis case? Actually, prey availability matches with brown trout emergence (in green) but flow and temperature conditions should be modified by Global Climate Change and delayed prey availability inducing a decrease in brown trout recruitments (in orange).

Figure 1

 1 Figure 1 Average drift rate of the three invertebrate taxa according to water velocity (A: low flow in dark, medium flow in grey and high flow in light), gravel bed quality (B: fine gravel in dark and coarse gravel in light), head capsule width (C: small individuals in dark and large individuals in light) and season (D: March in dark and October in light). Bars indicate minimum and maximum drift rate values.

Figure 2

 2 Figure2Model estimates of the drift probabilities of the three invertebrate taxa according to water velocity (cm s -1 ). Shaded areas are the 95% probability intervals of posterior distributions.

Figure 3

 3 Figure 3 Model estimates of the drift probabilities of the three invertebrate taxa according to head capsule width (mm). Shaded areas are the 95% probability intervals of posterior distributions.

Figure 4

 4 Figure 4 Model estimates of drift probabilities of the three invertebrate taxa according to the season for mean width individual and mean water velocity. Boxplots indicate the 1, 25, 50, 75 and 99 percentiles of posterior distributions.

Figure

  Figure S1 Estimates of the drift probabilities of the three invertebrate taxa according to body length (mm). Shaded areas are the 95% probability intervals of posterior distributions.

Figure

  Figure S2 Distribution of the head capsule width (in mm) for the three taxa and the two trials. Differences in HCW between March and October were not significant for Baetis (P(mean(HCWMarch) > mean(HCWOctober) = 0.671). Simulium were larger in March (P(mean(HCWMarch) > mean(HCWOctober) = 1.000) and Chironomus were larger in October (P(mean(HCWMarch) > mean(HCWOctober) = 0.000).

Chironomus

  and Similium larvae propensity to drift. The model presented in the article estimated an effect of the individual head capsule width (HCW) by taxon (γ). To test our hypotheses about the drift propensity of small Simulium and Chironomus (i.e. small Simulium leaving actively the channels and small Chironomus carried by the current), the model was modified to estimate an effect of the individual size (HCW) by taxon and by water velocity modality.

  stocks of carbohydrates are depleted, lipids are catabolised via the lipolysis pathway.Triglycerides contained in body fat release fatty acids in the blood, which are caught by the liver and oxidized in acetyl-Coenzyme A (acetyl-CoA) via the β-oxidation pathway. Acetyl-CoA is used for the synthesis of ketone bodies to feed muscles and the brain after transformation, or for energy synthesis. Finally, when carbohydrates and lipids are depleted, muscular proteins are mobilised. Muscles constitute the main protein mass and a reserve of amino acids. Muscle proteolysis releases amino acids in the blood that are catabolized by the liver via amino acid catabolism. Amino acid nitrogen components are eliminated in urea, while carbon skeletons are converted either into glucose via gluconeogenesis or into acetyl-CoA for the synthesis of energy. Acetyl-CoA obtained from the β-oxidation or from the catabolism of amino acids is invested first in Krebs cycle and then in the respiratory chain for the synthesis of ATP(Figure 2.1 -Simpkins et al. 2003;[START_REF] Hecketsweiler | Voyage en biochimie: circuits en biochimie humaine, nutritionnelle et métabolique[END_REF]). Synthesis of adenosine triphosphate (ATP) provides the energy needed for all the chemical reactions of metabolism. Fatty acid catabolism and amino acid catabolism can be activated sequentially but some organisms (and particularly larvae, as found in European plaice by[START_REF] Ehrlich | Chemical changes during growth and starvation of larval Pleuronectes platessa[END_REF] can use them in conjunction to preserve the lipid reserve (and dispose of it longer) as it is the most costeffective way to produce energy[START_REF] Bar | Physiological and hormonal changes during prolonged starvation in fish[END_REF]).

Figure 2 . 1

 21 Figure 2.1 Metabolic flows for all living organisms (including salmonids) in fasting situation (modified from Hecketsweiler & Hecketsweiler 2004).

  was conducted in the INRA experimental facilities (UMR Ecobiop, Saint-Péesur-Nivelle, France) authorized for animal experimentation (A640141). The experiments were in strict accordance with EU legal frameworks related to the protection of animals used for scientific research (Directive 2010/63/EU) and according to the National Guidelines for Animal Care of the French Ministry of Research.

Figure 1

 1 Figure 1 (a): Weight gain (WG = (final weightinitial weight) / initial weight) of emerged alevins according to rearing conditions. Boxplots indicate the 1, 25, 50, 75 and 99 percentiles. (b): Length (in cm) and weight (in g) of alevins after 9 days of growth. Boxplots indicate the 1, 25, 50, 75 and 99 percentiles of the initial measures of fish. The daily mean fish WG by tank for the fed conditions (F9, DF9 and F5) according to the mean number of prey (Chironomus) eaten daily by tank at 8°C (c) and 11°C (d). Dark items represent alevins reared at 8°C and light items represent alevins reared at 11°C. Fed fish F5 are represented in diamond, fed fish F9 are in circles, delayed-fed fish DF9 are in triangle and starved fish S9 are in square.

  Figure S1Mean and standard deviation of mRNA levels of genes involved in proteasome(Fbx32, MuRF1, MuRF2, MuRF3, Znf216), autophagy (atg4b, atg12l, SQSTM1, Mul1, Bnip3) pathways, fatty acid (HOAD, CPT1A, CPT1B) and amino-acid (GDH1, GDH2, GDH3, ASAT1, ASAT2, ALAT) catabolism. From yolk sac exhaustion, 6 fish per condition were sampled at the beginning (Initial conditions), after 5 days (F5, S5) and after 9 days (F9, S9, DF9) of growth at 8°C (dark grey) and at 11°C (light grey). Gene expression level was normalized by the abundance of exogenous luciferase RNA.

Figure 2 . 2

 22 Figure 2.2 Mean mRNA levels of genes involved in fatty acid (CPT1B), amino acid catabolism (GDH3), proteasome (MuRF2) and autophagy (SQSTM1) according to the treatment (Fed F9, Starved S9 or Delayed-First feeding DF9) after 9 days of experiment. mRNA levels were normalized by the abundance of exogenous luciferase RNA. Bars represent standard deviation. Alevins reared at 8°C are represented in dark grey and those reared at 11°C are in light grey.

  𝑊𝑒𝑖𝑔ℎ𝑡 𝑖 -𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑊𝑒𝑖𝑔ℎ𝑡 𝑖 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑊𝑒𝑖𝑔ℎ𝑡 𝑖 Fit of the correlation was calculated by dividing the variability unexplained by the correlation model with the variability observed in the data set as follows: 𝐹𝑖𝑡 = 1 -𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑎𝑡𝑎 𝑠𝑒𝑡

  Precipitation projections for 2100 from the Intergovernmental Panel on Climate Change (IPCC) forecast an increase of the average precipitation in Northern and Central Europe from October to March, while no change or a moderate reduction is expected in Southern Europe and Mediterranean (IPCC 2013). Then, Global Climate Change should increase precipitation during winter and spring in the northern distribution range of brown trout and freshwater ecosystems should face more frequent and extreme floods. Such events should lead to a decrease in the abundance of benthic invertebrates(Nislow et al. 2002; McMullen & Lytle 2012). As the number of invertebrates drifting is linked with benthic abundances (i.e. the benthic-drift hypothesis;[START_REF] Mclay | A study of drift in the Kakanui River, New Zealand[END_REF][START_REF] Hildebrand | The relation of drift to benthos density and food level in an artificial stream[END_REF] Kennedy et al. 2014), food availability for driftfeeding fish could be reduced. At the time of emergence, alevins have a limited amount of reserves and need to feed quickly to avoid mortality[START_REF] Cushing | The production cycle and the numbers of marine fish[END_REF] Skoglund & Barlaup 2006).

Figure 1

 1 Figure1Experimental setup in the semi-natural Lapitxuri channel. The upstream reach was the control, while a flood was simulated in the downstream reach. Four cages were used for the growth of alevins in March/early spring (in dark grey) and two in April-May/late spring (in light grey). Behavioural surveys were conducted in additional small cages (not shaded), close to the observation chambers during the two trials with alevins tagged differently (top right photos). The picture shows an overview of the device.

Figure 2

 2 Figure 2 Number of individuals per sample (average ± standard error) of the groups of invertebrates identified according to treatment (Control in white and Impacted by flood in grey) immediately and at the end of the trial.

Figure 3

 3 Figure 3 Raw data (A) of the survival probability and mean weight gain by cage and model estimates (B) of the survival probability, the individual weight gain and fish productivity in control (in white) and impacted (in grey) cages in the first and the second trial. Boxplots indicate the 1, 25, 50, 75 and 99 percentiles of posterior distributions. Significant differences are shown by *.

Figure 5

 5 Figure 5 Model estimations of the number of feeding attempts during a survey (five minutes) according to time (in days) in the control (in blue) or in the impacted conditions (in red). Shaded areas are the 95% probability intervals of posterior distributions.

Figure 7

 7 Figure 7 Model estimations of the number of aggressive interactions during five minutes according to time (in days) in the control (in blue) or in the impacted conditions (in red). Shaded areas are the 95% probability intervals of posterior distributions.

Figure 8

 8 Figure8Individual weight gain (WG) according to the number of survey during which alevins were in activity, the average number of attempts to catch prey by alevins and the average number of aggressive interactions by alevins. Activity of alevins was also related to the number of attempts to catch prey and the average aggressive interactions. Empty circles represent alevins in the control cages, grey circles the alevins in the impacted cages and the dotted line the correlation.

Figure

  Figure S2 Average individual weight gain of alevins by cage according to the average abundance of Diptera, Ephemeroptera and the total invertebrates by cage at the end of the trials. Empty circles represent the control cages, grey circles the impacted cages and the dotted line the correlation.

Figure 4 . 2

 42 Figure 4.2 Number of individuals per sample (average ± standard error) of the groups of invertebrates identified according to treatment (Control in white and Impacted by flood in grey) immediately and at the end of the trial.

Figure 4 . 3

 43 Figure 4.3 Model estimates of the survival probability, of standard deviation of the final weight, of weight gain of alevins and of fish productivity of control (in white) and impacted by flood reaches (in grey). Boxplots indicate the 1, 25, 50, 75 and 99 percentiles of posterior distributions. Significant differences are shown by *.

Figure 4 . 4

 44 Figure 4.4 Model estimations of the number of prey ingested and the number of Chironomidae ingested by alevins in the control (in white) and in the impacted (in grey) reaches. Boxplots indicate the 1, 25, 50, 75 and 99 percentiles of posterior distributions. Significant differences are shown by *.

Figure 1

 1 Figure 1 Stream productivity increases during the spring. Emergence is spread from early spring to late spring and alevin density is a trade-off between food availability and territoriality.

Figure 2

 2 Figure 2 Currently, fish emergence timing matches with invertebrate abundance (in black) but Global Climate Change should delay the increase in invertebrate abundance and advance the emergence date (in blue) and create a mismatch between prey availability and predator requirements (in red).

  highlighted that spawning time of Atlantic salmon varied along an altitudinal gradient in Scotland rivers. At high altitudes, temperatures were lower and salmons spawned earlier. The same trend was observed by Warren et al. (2012), warmer temperatures delayed spawning of brook trout (Salvelinus fontinalis) to coincide the date of emergence with the peak of prey production. These two phenomenon (i.e. a quicker development associated with a delayed spawning season) could compensate each other but it is still difficult to say whether the emergence date will be changed. The third mechanism affecting the timing of emergence is the variation in egg size. Johnston & Legget

Figure 3

 3 Figure 3 Effects of Global Climate Change on the proximal mechanisms affecting the emergence date (i.e. the egg development duration, the timing of the spawning season and the size of the eggs).

Table 1

 1 Water velocity (mean ± SD) for the 6 channels at the two trials.

	Flow	Gravel bed	Water velocity in March (cm s -1 )	Water velocity in October (cm s -1 )
	High	Coarse	36.71 ± 4.61	40.00 ± 2.38
	High	Fine	43.86 ± 4.60	37.86 ± 2.27
	Medium	Coarse	29.14 ± 4.85	29.86 ± 6.54
	Medium	Fine	29.57 ± 6.11	30.57 ± 5.91
	Low	Coarse	12.86 ± 4.56	17.71 ± 1.70
	Low	Fine	14.29 ± 1.80	12.43 ± 2.23

  𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖(𝑝. 𝐷 𝑖 ) 𝐿𝑜𝑔𝑖𝑡(𝑝. 𝐷 𝑖 ) = 𝜇 𝑗[𝑖] + 𝛼 𝑗[𝑖] 𝑉 𝑖 + 𝛽 𝑗[𝑖] 𝐻 𝑖 + 𝛾 𝑗[𝑖] 𝐻𝐶𝑊 𝑖 + 𝜂 𝑗[𝑖] 𝑆 𝑖 + 𝛿 𝑙[𝑖]

	1.0 A Parameters were given independent "weakly informative" priors (i.e. sampled in a normal Low Medium High Fine Coarse 1.0 B
	0.8 distribution N(0, 100) and for 𝜎 𝛿 in a truncated half-Cauchy t(0, 1, 1) distributions -Gelman & 0.8
	rate	0.6		rate	0.6
	Drift	0.4		Drift	0.4
		0.2			0.2
		0.0			0.0
		Baetis	Simulium	Chironomus	Baetis	Simulium	Chironomus
			Invertebrate taxa			Invertebrate taxa
		C			D
		1.0		Small Large	1.0	March	October
		0.8			0.8
	rate	0.6		rate	0.6
	Drift	0.4		Drift	0.4
		0.2			0.2
		0.0			0.0
		Baetis	Simulium	Chironomus	Baetis	Simulium	Chironomus
			Invertebrate taxa			Invertebrate taxa
	Where μj[i] was the average drifting probability and αj[i], βj[i], γj[i] and ηj[i] were fixed-effect
	parameters for water velocity (Vi), gravel bed quality (Hi), head capsule widths (HCWi) or body
	lengths (BLi) and season (Si) respectively. A random effect (δ) was drawn from a common
	distribution:
				𝛿 𝑙[𝑖] ~ 𝑁𝑜𝑟𝑚𝑎𝑙(0, 𝜎 𝛿 2 )

Table 2

 2

	Model selection based on minimum Deviance Information Criterion (DIC). Selected
	model is indicated by bold characters.
				Estimated
	Model	Deviance	DIC	parameter
				number
	Logit(p.Di) = μ	1645	1651	3.006
	Logit(p.Di) = μ + αj[i]Vi + γj[i]HCWi + δl[i]	1517	1578	30.46
	Logit(p.Di) = μ + αj[i]Vi + βj[i]Hi + γj[i]HCWi + δl[i]	1515	1579	32.06
	Logit(p.Di) = μ + αj[i]Vi + βj[i]Hi + γj[i]HCWi+ ηj[i]Si + δl[i] 1520	1576	27.88
	Logit

(p.Di) = μ + αj[i]Vi + γj[i]HCWi + ηj[i]Si + δl[i] 1521

			1573	26
	Logit(p.Di) = μ + αj[i]Vi + γj[i]BLi + δl[i] + ηj[i]Si	1525	1578	26.34

Table 3

 3 Main statistics of the posterior probability distribution functions of the free parameters.

	Parameters are considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).
	Parameters			Mean	SD	2.5%	Median 97.5% P(X > 0)
	Water velocity	αB αS αC	Baetis Simulium Chironomus	0.019 0.018 0.042	0.016 0.018 0.014	-0.012 -0.017 0.014	0.019 0.017 0.042	0.051 0.054 0.071	0.889 0.841 0.997
		γB	Baetis	0.183	0.655	-1.101	0.181	1.469	0.609
	HCW	γS	Simulium	-6.512	1.804	-10.130 -6.493	-3.008 0.000
		γC	Chironomus	-4.979	2.215	-9.316	-4.987	-0.656 0.012
		ηB	Baetis	1.208	0.341	0.552	1.203	1.900	0.999
	Season	ηS	Simulium	-1.216	0.405	-2.031	-1.209	-0.443 0.001
		ηC	Chironomus	-0.253	0.309	-0.866	-0.253	0.356	0.200
	SD of the
	random	σδ		0.396	0.129	0.158	0.390	0.667	1.000
	effects

Table S2

 S2 by step experimental procedure with the sample number and if it is included (Y) or not (N) in the model.

	δ		Taxon	Flow	Season	Gravel bed	P(δ[X] > 0)
	N° sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28	Included in the model Baetis 12 th of March 2015 High Baetis High N Addition of invertebrates Baetis Medium Baetis Medium N Opening of the flow at low level Baetis Low Baetis Low N Baetis High N Gradual elevation of flow Baetis High Y Baetis Medium Y Baetis Medium Y Baetis Low Y Baetis Low Y Simulium High Y Simulium High Y Simulium Medium Y Simulium Medium Y Simulium Low Y Dusk Simulium Low Y Simulium High Y Simulium High Y Simulium Medium Y Simulium Medium Y Simulium Low Y Simulium Low Y Chironomus High Y Chironomus High Y Chironomus Medium Y Chironomus Medium Y Chironomus Low Y Chironomus Low Y Dawn Chironomus High Y Chironomus High Chironomus Medium Y Chironomus Medium Chironomus Low	Time March 22 nd of October 2015 Coarse March Fine 09:00 March Coarse Addition of March Fine invertebrates 10:00 March Coarse Opening of the flow March Fine at low level 11:00 October Coarse 12:00 Gradual elevation of flow October Fine 12:15 October Coarse 12:30 October Fine 12:45 October Coarse 13:00 October Fine 14:00 March Coarse 15:00 March Fine 16:00 March Coarse 17:00 March Fine 18:00 March Coarse 19:00 March Fine 19:30 Dusk October Coarse 20:00 October Fine 21:00 October Coarse 22:00 October Fine 23:00 October Coarse 00:00 October Fine 01:00 March Coarse 02:00 March Fine 03:00 March Coarse 04:00 March Fine 05:00 March Coarse 06:00 March Fine October Coarse 06:30 07:00 October Fine 07:30 Dawn October Coarse 08:00 October Fine October Coarse	N° sample 0.121 0.381 0.323 0.924 0.442 1 0.753 2 0.842 3 0.749 4 0.097 5 0.297 6 0.676 7 0.310 8 0.385 9 0.233 0.505 0.470 0.765 0.665 0.891 0.829 0.137 0.391 0.275 0.441 0.681 0.370 0.430 0.371 0.538 0.634 0.750 0.099 0.936 0.504 0.209	Included in the model N N N N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
	29 30	Y Y	Chironomus Low	09:00 10:00 October	Fine	0.494	Y Y
	31	Y			11:00			Y
	32	Y		End of the experiment	12:00	End of the experiment		Y
			Recovery of the		Recovery of the
	33	Y	invertebrates settled	invertebrates settled		Y
			in the channels		in the channels

Probabilities of the random effects (the residuals) to be different from 0. Parameters are considered significant when P(δ[X] > 0) < 0.10 or P(δ[X] > 0) > 0.90 and are indicated in bold characters.

Table 1 .

 1 1 Synthesis of the main results on the drift propensity of Baetis, Simulium and Chironomus according to water velocity, gravel bed, individual size and season.

	Taxa	Mean drift probability	Water velocity 14, 30, 40 cm.s -1	Gravel bed Fine vs. Coarse	Individual size Head capsule width	Season Spring vs. Autumn
	Baetis	0.684	-	-	-	↗ Drift in Autumn
					Small individuals
	Simulium	0.128	-	-	drift more Active: not enough	↗ Drift in Spring
					organic matter
	Chironomus	0.363	↗ Water velocity ↗ Drift rate	-	Small individuals drift more Passive: carried by
					the current

-Further considerations and discussion

Results included in the MS showed that head capsule width interfered with water velocity in

Table 1 .

 1 2 Main statistics of the posterior probability distribution functions of the effect of the individual size (γ) by taxon (Simulium and Chironomus) and by water velocity modality(low, medium and high). Parameters are considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	HCW parameters Taxon	Water velocity modality Mean	P(X > 0)
	γS1		Low	-12.520	0.000
	γS2	Simulium	Medium	-3.400	0.122
	γS3		High	-3.896	0.096
	γC1		Low	-0.241	0.468
	γC2	Chironomus	Medium	-6.517	0.048
	γC3		High	-7.899	0.013

Figure 1.1 Model estimates of the drift probabilities of Simulium and Chironomus according to head capsule width (mm) at low (blue circles), medium (orange triangles) and high (grey squares) water velocities.

Table 1

 1 Comparison of the gene expressions among rearing conditions. Genes involved in fatty acid and amino acid catabolism, proteasome and autophagy were studied (pathways and genes were displayed in the first columns). Fold Changes (FC) were calculated by dividing the mean expression of the condition 1 by the mean expression of the condition 2. Significant differences are in bold and * means that P(Condition 1 > Condition 2) is between 0.90 and 0.95 and ** means that P(Condition 1 > Condition 2) is between 0.95 and 1.00.

	FC values	FC1	FC2	FC3	FC4	FC5	FC6	FC7	FC8	FC9	FC10	FC11	FC12	FC13
	Pathways	Genes	F5 / S5 8°C	F5 / S5 11°C	F9 / S9 8°C	F9 / S9 11°C	F9 / DF9 F9 / DF9 S9 / DF9 S9 / DF9 8°C 11°C 8°C 11°C	F5 / F5 8/11°C	S5 / S5 8/11°C	F9 / F9 8/11°C	S9 / S9 DF9 / DF9 8/11°C 8/11°C
		Fbx32	0.10**	0.14**	0.20**	0.11**	0.55*	0.19**	2.82**	1.64**	0.55**	0.78	1.20	0.70*	0.41**
		MuRF1	0.10**	0.16**	0.10**	0.18**	0.33**	0.26**	3.17**	1.49*	0.33**	0.52**	0.67*	1.14	0.54**
	Proteasome	MuRF2	0.20**	0.14**	0.12**	0.06**	0.48**	0.12**	3.97**	1.88**	1.08	0.73	1.17	0.61*	0.29**
		MuRF3	0.30**	0.23**	0.22**	0.13**	0.48**	0.23**	2.24**	1.74**	0.72**	0.56**	1.21	0.75*	0.59**
		Znf216	0.84	0.65**	0.32**	0.32**	0.57**	0.60**	1.76**	1.89**	1.05	0.82	0.69	0.67*	0.72
		atg4b	0.38**	0.18**	0.10**	0.08**	0.28**	0.12**	2.73**	1.63**	1.45*	0.70	0.78	0.56*	0.34**
		atg12l	0.95	0.75*	0.49**	0.51**	0.67**	0.66**	1.37*	1.30	1.15	0.92	0.64*	0.66**	0.62**
	Autophagy	SQSTM1	0.65	0.70*	0.33**	0.21**	0.43**	0.52*	1.30	2.48**	0.78*	0.84	0.57	0.36	0.69*
		Mul1	0.98	0.81*	0.49**	0.68**	0.47**	0.77	0.96	1.13	1.16	0.95	0.58*	0.80	0.95
		Bnip3	0.42	0.28**	0.20**	0.19**	0.55**	0.33**	2.71**	1.75**	1.03	0.67*	0.82	0.76*	0.49**
	Fatty acid catabolism	HOAD CPT1A CPT1B	1.21 0.62** 0.53**	0.98 0.44** 0.46**	1.32* 1.02 0.91	1.49** 1.76* 3.11**	1.31 1.50 1.02	0.93 1.26 1.08	0.99 1.47 1.12	0.63** 0.72 0.35**	1.13* 2.26** 1.31	0.92 1.59* 1.13	1.25** 1.19 1.56*	1.41** 2.04** 5.32**	0.89 1.00 1.66**
		GDH1	1.17	0.96	1.10	1.08	1.33**	0.80*	1.21	0.74**	1.03	0.84	1.13*	1.12*	0.68
		GDH2	1.04	0.87	1.05	0.97	1.28*	0.75**	1.22	0.78*	1.02	0.85	1.10*	1.01	0.65
	Amino acid	GDH3	1.65**	1.30*	1.39**	1.82**	1.44**	1.16	1.04	0.64**	1.11	0.88	1.02	1.33**	0.82
	catabolism	ASAT1	1.54**	1.42**	1.17	1.75**	1.31**	1.21	1.12	0.69**	0.97	0.90	0.93	1.38**	0.85
		ASAT2	1.23*	1.05	0.92	1.26	1.20	0.97	1.31**	0.77*	0.97	0.83	0.93	1.27	0.75
		ALAT	1.16	0.82	1.13	1.37**	1.57**	0.96	1.38**	0.70**	1.26	0.89	1.33	1.61**	0.81

Table S2 :

 S2 Mean and standard deviation of mRNA levels of genes involved in fatty acid/amino-acid catabolism and proteasome/autophagy pathways. From yolk sac exhaustion, 6 fish per condition were sampled at the beginning (D0), after 5 days (F5, S5) and after 9 days (F9, S9, DF9) of growth at 8°C and at 11°C. Gene expression level was normalized by the abundance of exogenous luciferase RNA and has no unit. The mean low expressions are in white, intermediate expressions in light grey and high expression in dark grey. Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Proteasome Fbx32 0.646 0.24 0.430 0.10 0.089 0.13 0.160 0.15 0.367 0.21 0.306 0.17 0.896 0.33 1.153 0.46 1.877 0.67 2.675 1.00 0.666 0.31 1.630 1.16 MuRF1 0.508 0.09 0.335 0.08 0.101 0.04 0.302 0.18 0.247 0.14 0.371 0.22 0.964 0.40 1.848 0.84 2.384 0.81 2.088 0.99 0.752 0.47 1.401 1.05 MuRF2 0.767 0.39 0.698 0.22 0.192 0.05 0.178 0.06 0.236 0.13 0.202 0.11 0.949 0.31 1.297 0.43 1.960 0.71 3.237 1.21 0.493 0.12 1.725 1.19 MuRF3 0.799 0.12 0.652 0.09 0.341 0.04 0.472 0.17 0.389 0.12 0.321 0.13 1.149 0.38 2.067 0.59 1.801 0.53 2.388 0.90 0.803 0.36 1.369 0.65 Znf216 0.754 0.21 0.753 0.14 0.621 0.11 0.591 0.17 0.480 0.15 0.697 0.29 0.744 0.14 0.905 0.25 1.485 0.65 2.203 0.27 0.843 0.27 1.164 0.61

			Initial Conditions -D0	F5		F9		S5		S9		DF9
	Pathways	Genes	8°C	11°C	8°C	11°C	8°C	11°C	8°C	11°C	8°C	11°C	8°C	11°C
	Mean SD Autophagy
		Bnip3 0.678 0.27 0.570 0.15 0.320 0.08 0.310 0.10 0.355 0.17 0.435 0.20 0.754 0.20 1.117 0.31 1.742 0.95 2.305 0.53 0.644 0.09 1.319 0.80
		HOAD 1.418 0.56 1.710 0.41 1.208 0.32 1.067 0.35 0.872 0.32 0.696 0.27 0.997 0.18 1.089 0.46 0.660 0.27 0.467 0.17 0.665 0.10 0.745 0.15
	Fatty acid catabolism	CPT1A 1.995 0.63 2.273 0.66 0.991 0.31 0.438 0.15 0.576 0.32 0.486 0.29 1.585 0.60 0.999 0.42 0.562 0.28 0.276 0.10 0.384 0.12 0.384 0.26
		CPT1B 1.510 0.37 1.670 0.40 0.652 0.21 0.499 0.15 0.747 0.37 0.478 0.20 1.228 0.30 1.089 0.36 0.818 0.38 0.154 0.09 0.732 0.20 0.442 0.10
		GDH1 0.801 0.28 0.959 0.14 0.955 0.18 0.929 0.30 0.872 0.29 0.772 0.31 0.814 0.09 0.972 0.41 0.794 0.17 0.712 0.18 0.656 0.10 0.966 0.17
		GDH2 0.780 0.22 0.959 0.14 0.929 0.19 0.911 0.30 0.837 0.24 0.764 0.32 0.893 0.27 1.048 0.44 0.798 0.21 0.787 0.20 0.655 0.10 1.015 0.16
	Amino acid	GDH3 1.015 0.22 1.209 0.20 1.318 0.14 1.185 0.29 0.945 0.36 0.928 0.42 0.799 0.09 0.913 0.29 0.678 0.18 0.510 0.20 0.655 0.18 0.799 0.13
	catabolism	ASAT1 0.921 0.24 1.017 0.13 1.163 0.21 1.199 0.30 0.898 0.35 0.970 0.40 0.757 0.08 0.845 0.32 0.766 0.20 0.554 0.11 0.685 0.12 0.803 0.08
		ASAT2 0.894 0.29 0.934 0.18 0.955 0.23 0.983 0.30 0.754 0.24 0.811 0.27 0.776 0.09 0.938 0.43 0.822 0.24 0.644 0.15 0.626 0.10 0.838 0.12
		ALAT 0.963 0.27 0.892 0.38 1.030 0.12 0.820 0.21 0.871 0.25 0.654 0.23 0.885 0.08 0.996 0.27 0.768 0.10 0.477 0.26 0.555 0.09 0.684 0.08

atg4b 0.610 0.28 0.664 0.18 0.357 0.08 0.245 0.09 0.224 0.12 0.286 0.15 0.951 0.35 1.350 0.29 2.146 1.24 3.801 1.58 0.787 0.12 2.328 1.19 atg12l 0.941 0.18 0.767 0.13 0.825 0.16 0.714 0.15 0.474 0.22 0.744 0.21 0.872 0.15 0.949 0.18 0.965 0.33 1.465 0.58 0.704 0.14 1.126 0.43 SQSTM1 0.779 0.19 1.698 0.44 1.098 0.54 1.413 0.95 0.735 0.13 1.289 0.64 1.689 1.11 2.021 0.77 2.202 1.33 6.108 1.70 1.694 0.74 2.465 1.99 Mul1 1.651 0.88 1.443 0.29 1.131 0.33 0.976 0.51 0.470 0.32 0.803 0.24 1.153 0.20 1.211 0.33 0.954 0.44 1.187 0.43 0.992 0.19 1.049 0.34

Table 2 .

 2 1 Synthesis of the main results on the growth and metabolism of starved and delayed first-feeding alevins at 8 and 11°C.

	Temperature Starvation		Delayed First-feeding
		↘ Growth	Biometric data Recovery
	8°C	Similar to F & DF fish Body reserves still available?	Lipid & AA catabolism	Similar to F fish
		+++	Autophagy & Proteasome	+
		↘↘ Growth	Biometric data Contrasted recovery
	11°C	↘↘ Gene expression Reserves depleted? Hypometabolism?	Lipid & AA catabolism	Intermediate state
		++++	Autophagy & Proteasome	++

Table 2 .

 2 2 Correlations between mRNA levels (Y) and individual weight gain (Wg; X) of alevins, mean of posterior probability distribution functions of a (the slope coefficient), b (the intercept) and fit of the models. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	Conditions	Pathways	Genes	a	P(a > 0)	b	P(b > 0)	Equations	Fit
			Fbx32	-2.33	0.02	0.86	1.00	Fbx32 = -2.329 Wg + 0.8606	0.29
			MuRF1	0.40	0.63	0.22	0.77	MuRF1 = 0.402 Wg + 0.2188	-0.15
		Proteasome	MuRF2	-0.22	0.39	0.27	0.92	MuRF2 = -0.2178 Wg + 0.2678	-0.16
			MuRF3	-0.42	0.31	0.45	0.98	MuRF3 = -0.4177 Wg + 0.4493	-0.14
	F9		Znf216 atg4b	0.86 -0.02	0.70 0.49	0.39 0.26	0.85 0.88	Znf216 = 0.8641 Wg + 0.3935 atg4b = -0.01875 Wg + 0.2598	-0.12 -0.16
			atg12l	2.19	0.91	0.12	0.63	atg12l = 2.187 Wg + 0.1172	0.08
		Autophagy	SQSTM1	2.14	0.75	0.53	0.76	SQSTM1 = 2.142 Wg + 0.5299	-0.08
			Mul1	1.41	0.75	0.32	0.75	Mul1 = 1.406 Wg + 0.3199	-0.09
			Bnip3	0.45	0.64	0.29	0.84	Bnip3 = 0.4489 Wg + 0.2939	-0.14
			Fbx32	-7.97	0.07	2.14	1.00	Fbx32 = -7.969 Wg + 2.136	0.18
			MuRF1	-4.29	0.18	2.28	1.00	MuRF1 = -4.287 Wg + 2.279	0.00
		Proteasome	MuRF2	-8.63	0.09	2.42	1.00	MuRF2 = -8.627 Wg + 2.422	0.17
			MuRF3	-5.71	0.11	2.03	1.00	MuRF3 = -5.711 Wg + 2.034	0.09
	S9		Znf216 atg4b	-4.12 -11.75	0.16 0.06	1.73 2.81	1.00 1.00	Znf216 = -4.124 Wg + 1.725 atg4b = -11.75 Wg + 2.808	0.01 0.26
			atg12l	-5.88	0.03	1.14	1.00	atg12l = -5.88 Wg + 1.137	0.27
		Autophagy	SQSTM1	-11.72	0.10	3.63	1.00	SQSTM1 = -11.72 Wg + 3.626	0.21
			Mul1	-5.40	0.03	0.95	1.00	Mul1 = -5.399 Wg + 0.9494	0.26
			Bnip3	-7.21	0.07	1.89	1.00	Bnip3 = -7.207 Wg + 1.888	0.18
			Fbx32	-9.52	0.00	1.60	1.00	Fbx32 = -9.517 Wg + 1.596	0.70
			MuRF1	-9.25	0.00	1.51	1.00	MuRF1 = -9.253 Wg + 1.511	0.81
		Proteasome	MuRF2	-10.82	0.00	1.62	1.00	MuRF2 = -10.82 Wg + 1.616	0.77
			MuRF3	-6.45	0.00	1.39	1.00	MuRF3 = -6.451 Wg + 1.389	0.82
	DF9		Znf216 atg4b	-4.78 -11.60	0.00 0.00	1.23 2.10	1.00 1.00	Znf216 = -4.777 Wg + 1.227 atg4b = -11.6 Wg + 2.102	0.64 0.74
			atg12l	-4.09	0.00	1.11	1.00	atg12l = -4.09 Wg + 1.107	0.76
		Autophagy	SQSTM1	-10.89	0.01	2.58	1.00	SQSTM1 = -10.89 Wg + 2.584	0.44
			Mul1	-0.31	0.38	1.04	1.00	Mul1 = -0.3092 Wg + 1.035	-0.15
			Bnip3	-6.89	0.00	1.31	1.00	Bnip3 = -6.891 Wg + 1.306	0.76

Table 1

 1 Date, conditions and events at each step of the experiment for both trials (in early spring and in late spring).

	1 st trial -Early spring	2 nd trial -Late spring

Table 2

 2 Parameters taken into account according to the season and the condition. μ.Variable is the mean of the variable of interest, α represents the flood effect, β the second trial effect and γ the flood*second trial effect.If Abondi is the number of individuals counted in the i th sample, we assumed: 𝜇. log(𝐴𝑏𝑜𝑛𝑑 𝑖 + 1) 𝑖 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑖 + 𝛽 𝑆𝑒𝑎𝑠𝑜𝑛 𝑖 + 𝛾 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑖

	Season

Table 3

 3 Parameters taken into account according to the condition and the position of the behavioural cage. μ.Variable is the mean of the behaviour of interest, δ1 represents the increase in the control cages, δ2 the increase in the impacted cages and λ the downstream cage position effect.

			Position of the behavioural cage in the reach
			Upstream	Downstream
	Conditions	Control Flood	μ.Variable + δ1 μ.Variable + δ2	μ.Variable + δ1 + λ μ.Variable + δ2 + λ
	If p.Ak is the probability of being active (swimming in the water column) for an alevins during
	the k th survey, we assumed:
			𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑘 ~ 𝐵𝑒𝑛𝑜𝑢𝑙𝑙𝑖(𝑝. 𝐴 𝑘 )
		𝐿𝑜𝑔𝑖𝑡(𝑝. 𝐴 𝑘 ) = 𝜇 + 𝛿 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛[𝑘] 𝑇𝑖𝑚𝑒 + 𝜆 𝑃𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑘

Table 5

 5 Mean of the posterior probability distribution functions of the effect of flood (α), season (β) and the interaction flood*season (γ) on the survival probability, on individual weight gain of alevins and on fish productivity. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	Alevins performance	Parameters	Mean	P(X > 0)
		α	-0.963	0.004
	Survival probability	β	1.086	0.984
		γ	2.373	0.995
	Individual weight gain	α β γ	-0.046 1.192 -0.938	0.100 1.000 0.000
		α	-0.965	0.110
	Fish productivity	β	2.161	0.964
		γ	-0.618	0.294

Table S1 :

 S1 Water depth (in cm) and water velocities (in m s -1 ) recorded on the Lapitxuri brook (the 30/01/2015).TableS2Number of surveys, initial and final weight, individual weight gain, mean number of feeding attempts and of aggressive interactions of the alevins by survey during the first trial (early spring) according to their cage of origin and the colour of their tag.

					Lapitxuri brook -30/01/2015 -Nivelle discharge = 55.6 m 3 s -1
	Section Water depth (cm) 13 1 Number Condition Cage VIE tag of surveys	2 13 Initial weight (mg)	3 30 Final weight (mg)	4 32 Individual weight gain	5 38 Mean feeding attempts by survey 6 7 47 50 Short Medium Long Total	8 62 Mean number of aggressive interactions by survey 9 10 11 70 50 30 Attacks made Attacks sustained Total
	Water velocity (m s -1 ) at Control	80% water depth 0.45 Green 40 40% water depth 1 Orange 40 20% water depth Up Blue 13 White 2 Green 40	0.48	146 127 --131	0.66 0.55 0.5	0.79 0.327 0.63 0.085 0.54 --0.083	0.83 3.40 0.82 2.90 0.75 0.54 0.00 3.25	4.28 2.25 0.00 0.00 3.03	0.89 0.87 0.7	0.82 3.43 11.10 1 1.35 6.50 0.93 0.08 0.62 0.00 0.00 2.68 8.95	0.87 2.18 1.06 0.43 1.09 0.00 0.00 0.45	0.66 0.03 0.83 2.35 0.82 0.69 0.00 0.25	0.62 0.72 0.6	2.20 0.38 2.78 0.37 0.69 0.43 0.00 0.70
		2	Orange 40		153		0.297	3.25	2.53		1.48 7.25	0.98	1.03	2.00
		Down	Blue	36		-		-	2.33	1.39		0.89 4.61	0.22	0.33	0.56
			White 35		-		-	2.91	2.80		1.69 7.40	0.57	0.17	0.74
			Green 31		-		-	0.81	0.23		0.00 1.03	0.03	0.13	0.16
		3	Orange 39		112		0.143	3.21	3.28		1.67 8.15	1.00	0.03	1.03
		Up	Blue	23		-		-	1.96	0.57		0.35 2.87	0.04	0.39	0.43
	Flood		White 35 Green 40		187 136		0.748 0.193	2.31 3.43	1.94 2.80		1.51 5.77 2.00 8.23	0.14 0.43	0.74 0.68	0.89 1.10
		4	Orange 40		141		0.226	4.48	3.15		2.00 9.63	1.30	0.38	1.68
		Down	Blue	32		-		-	2.81	1.41		0.88 5.09	0.06	0.41	0.47
			White 35		-		-	3.06	3.03		2.26 8.34	1.40	0.51	1.91

Table S3

 S3 Correlations between fish variable (Y) and invertebrate abundances after the flood (X), mean of posterior probability distribution functions of a (the slope coefficient) and b (the intercept) and fit of the models just after flood simulation disturbance. Parameters in bold are significant (when P(X > 0) is less than 0.10 or above 0.90).TableS4Correlations between fish variable (Y) and invertebrate abundances at the end of the trials (X), mean of posterior probability distribution functions of a (the slope coefficient) and b (the intercept) and fit of the models at the end of the trials. Parameters in bold are significant (when P(X > 0) is less than 0.10 or above 0.90).

	Variable Y Variable Y	Variable X Variable X	Groups Groups	a	a	P(a > 0) P(a > 0)	b b	P(b > 0) P(b > 0)	Equation Equation	Fit Fit
			Diptera Diptera	0.591 0.094	0.967 0.887	7.438 10.490	0.969 0.986	S = 0.591 Diptera + 7.438 S = 0.09432 Diptera + 10.49	0.574 0.649
			Trichoptera Trichoptera	-1.515 -0.356	0.086 0.323	14.880 14.440	0.997 0.998	S = -1.515 Trichoptera + 14.88 S = -0.3557 Trichoptera + 14.44	0.580 0.621
			Ephemeroptera 0.201 Ephemeroptera -0.002	0.820 0.455	8.719 13.750	0.957 0.997	S = 0.2005 Ephemeroptera + 8.719 S = -0.002252 Ephemeroptera + 13.75	0.313 0.582
	Number of Number of		Mollusca Mollusca	3.306 0.667	0.870 0.691	8.436 12.030	0.964 0.988	S = 3.306 Mollusca + 8.436 S = 0.6673 Mollusca + 12.03	0.407 0.581
	surviving alevins surviving alevins	Number of invertebrates Number of invertebrates	Coleoptera Coleoptera	1.084 0.611	0.998 0.915	0.362 7.091	0.546 0.894	S = 1.084 Coleoptera + 0.3619 S = 0.6105 Coleoptera + 7.091	0.832 0.654
	by cage by cage	After the flood At the end of the trials	Plecoptera Plecoptera	0.026 3.934	0.493 0.850	11.480 10.480	0.990 0.988	S = 0.02616 Plecoptera + 11.48 S = 3.934 Plecoptera + 10.48	0.342 0.661
	S S		Oligochaeta Oligochaeta	0.026 0.221	0.515 0.956	11.100 9.253	0.971 0.984	S = 0.02557 Oligochaeta + 11.1 S = 0.2205 Oligochaeta + 9.253	0.256 0.727
			Crustacea Crustacea	0.128 -0.090	0.772 0.254	9.943 14.930	0.982 0.998	S = 0.1276 Crustacea + 9.943 S = -0.08984 Crustacea + 14.93	0.289 0.648
			Others Others	0.053 -0.005	0.660 0.401	10.420 13.960	0.980 0.997	S = 0.05349 Others + 10.42 S = -0.004778 Others + 13.96	0.251 0.598
			Total Total	0.122 0.052	0.960 0.875	4.947 8.776	0.854 0.945	S = 0.1218 Total + 4.947 S = 0.05168 Total + 8.776	0.525 0.627
			Diptera Diptera	0.025 0.011	0.974 0.999	-0.168 -0.602	0.223 0.006	WG = 0.02543 Diptera + -0.1681 WG = 0.01121 Diptera + -0.6015	0.459 0.914
			Trichoptera Trichoptera	0.844 0.188	0.922 1.000	-0.079 -0.083	0.386 0.107	WG = 0.844 Trichoptera + -0.0787 WG = 0.1882 Trichoptera + -0.08306	0.076 0.939
			Ephemeroptera 0.000 Ephemeroptera 0.090	0.500 0.991	0.251 -0.296	0.713 0.076	WG = -0.0001604 Ephemeroptera + 0.2508 -0.649 WG = 0.08964 Ephemeroptera + -0.2962 0.712
	Mean Individual Mean Individual		Mollusca Mollusca	-0.023 -0.483	0.469 0.090	0.280 0.978	0.737 0.943	WG = -0.02336 Mollusca + 0.2798 WG = -0.4834 Mollusca + 0.9781	-0.641 0.027
	Weight Gain Weight Gain	Number of invertebrates Number of invertebrates	Coleoptera Coleoptera	-0.005 -0.006	0.459 0.469	0.308 0.323	0.684 0.633	WG = -0.004779 Coleoptera + 0.3083 WG = -0.006134 Coleoptera + 0.3234	-0.637 -0.631
	by cages by cages	After the flood At the end of the trials	Plecoptera Plecoptera	1.022 -0.442	0.766 0.146	0.080 0.545	0.598 0.923	WG = 1.022 Plecoptera + 0.08009 WG = -0.442 Plecoptera + 0.5448	-0.386 -0.197
	WG WG		Oligochaeta Oligochaeta	0.014 0.005	0.970 0.994	-0.143 -0.169	0.261 0.123	WG = 0.01387 Oligochaeta + -0.1428 WG = 0.005161 Oligochaeta + -0.1686	0.416 0.773
			Crustacea Crustacea	-0.003 0.017	0.417 0.904	0.303 -0.221	0.802 0.284	WG = -0.003036 Crustacea + 0.3032 WG = 0.01688 Crustacea + -0.2214	-0.638 -0.005
			Others Others	0.038 0.110	0.782 0.971	-0.037 -0.742	0.467 0.068	WG = 0.03778 Others + -0.03725 WG = 0.1102 Others + -0.7421	-0.402 0.425
			Total Total	0.007 0.003	0.942 0.998	-0.404 -0.427	0.166 0.012	WG = 0.00711 Total + -0.404 WG = 0.003139 Total + -0.4272	0.189 0.888
			Diptera Diptera	-0.536 -0.333	0.172 0.014	11.800 21.350	0.952 0.996	Prod = -0.5361 Diptera + 11.8 Prod = -0.3325 Diptera + 21.35	0.040 0.724
			Trichoptera Trichoptera	4.990 5.049	1.000 1.000	0.312 -0.080	0.628 0.466	Prod = 4.99 Trichoptera + 0.3116 Prod = 5.049 Trichoptera + -0.07976	0.978 0.977
			Ephemeroptera 0.428 Ephemeroptera 0.188	0.898 0.996	3.072 2.488	0.701 0.767	Prod = 0.4279 Ephemeroptera + 3.072 Prod = 0.1877 Ephemeroptera + 2.488	-0.034 0.657
	Fish Productivity by cages Prod Fish Productivity by cages Prod	Number of invertebrates After the flood Number of invertebrates At the end of the trials	Mollusca Coleoptera Plecoptera Oligochaeta Mollusca Coleoptera Plecoptera Oligochaeta	-1.819 -0.522 14.100 -0.152 3.988 0.142 4.670 -0.180	0.332 0.226 0.969 0.312 0.965 0.555 0.772 0.260	9.490 13.490 3.602 9.922 -0.116 6.813 4.737 11.400	0.921 0.925 0.805 0.913 0.489 0.783 0.771 0.924	Prod = -1.819 Mollusca + 9.49 Prod = -0.5224 Coleoptera + 13.49 Prod = 14.1 Plecoptera + 3.602 Prod = -0.1516 Oligochaeta + 9.922 Prod = 3.988 Mollusca + -0.1155 Prod = 0.1418 Coleoptera + 6.813 Prod = 4.67 Plecoptera + 4.737 Prod = -0.1801 Oligochaeta + 11.4	-0.111 0.068 0.503 -0.159 0.267 -0.217 -0.062 -0.049
			Crustacea Crustacea	-0.196 -0.353	0.226 0.140	9.917 12.100	0.940 0.962	Prod = -0.1961 Crustacea + 9.917 Prod = -0.3531 Crustacea + 12.1	-0.116 0.094
			Others Others	0.288 0.127	0.941 0.999	3.862 1.887	0.778 0.744	Prod = 0.2876 Others + 3.862 Prod = 0.1274 Others + 1.887	0.117 0.761
			Total Total	-0.264 -0.099	0.010 0.132	25.580 17.900	0.997 0.955	Prod = -0.2642 Total + 25.58 Prod = -0.09919 Total + 17.9	0.819 0.313

  𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑝. 𝐹 𝑗 , 𝑆 𝑗 ) 𝑅1 𝑗 = 𝑆 𝑗 -𝐶1 𝑗 ; 𝐶2 𝑗 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑝. 𝐹 𝑗 , 𝑅1 𝑗 ) 𝑅2 𝑗 = 𝑅1 𝑗 -𝐶2 𝑗 ; 𝐶3 𝑗 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑝. 𝐹 𝑗 , 𝑅2 𝑗 ) 𝑅3 𝑗 = 𝑅2 𝑗 -𝐶3 𝑗 ; 𝐶4 𝑗 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑝. 𝐹 𝑗 , 𝑅3 𝑗 ) Then, if p.Sj is the probability of survival in the j reach and Nj the number of fish introduced in each reach at the beginning of the experiment, we assume: 𝑆 𝑗 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(𝑝. 𝑆 𝑗 , 𝑁 𝑗 ) 𝜇. 𝐿𝑜𝑔𝑖𝑡(𝑝. 𝑆 𝑗 ) = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑗 + 𝛽 𝑗

Table 4 .

 4 1 Mean of the posterior probability distribution functions of α, the effect of flood on the logarithm of the abundance of invertebrates. The effect was estimated for each group and for the total abundance, immediately after the flood and at the end of the trial. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	Groups	After the flood α1	P(α1 > 0)	End of the trial α2	P(α2 > 0)
	Diptera	-0.375	0.264	0.078	0.553
	Trichoptera	-0.440	0.163	-0.724	0.117
	Ephemeroptera	-0.844	0.156	-1.307	0.037
	Mollusca	0.015	0.512	-1.282	0.052
	Coleoptera	-0.680	0.054	-0.737	0.122
	Plecoptera	-0.500	0.246	-0.565	0.208
	Oligochaeta	0.014	0.503	-0.763	0.232
	Crustacea	-0.185	0.370	-0.616	0.196
	Exogenous	0.060	0.545	-0.402	0.281
	Total	-0.301	0.186	-0.519	0.168
	Effect of the flood on alevins

Table 4

 4

	.2 Mean of the posterior probability distribution functions of α, the effect of flood on the
	survival probability, on individual weight gain of alevins and on fish productivity. Parameters
	were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).
	Effect of the flood (α) on …	Mean	P(α > 0)
	Survival probability	-1.169	0.012
	Weight gain	0.443	0.907
	Productivity	-1.923	0.084

Table 4 .

 4 3 Correlations between invertebrate abundances immediately after the flood (X) and fish variable (Y), mean of posterior probability distribution functions of a (the slope coefficient) and b (the intercept) and fit of the models. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	Fish variable (Y)	Invertebrate variable (X)	Groups	a	P(a > 0)	b	P(b > 0)	Equations	Fit
			Diptera	0.009 0.912	23.890 1.000	S = 0.008572 Diptera + 23.89	-0.198
			Trichoptera	0.028 0.951	22.680 1.000	S = 0.02825 Trichoptera + 22.68	-0.127
			Ephemeroptera	0.016 0.967	23.510 1.000	S = 0.01583 Ephemeroptera + 23.51	0.034
	Number of surviving alevins by reach S	Abundance of invertebrates by reach Immediately after the flood	Mollusca Coleoptera Plecoptera Oligochaeta	0.008 0.943 0.011 0.973 0.198 0.952 0.040 0.917	19.440 0.996 19.530 0.998 21.610 0.999 21.340 0.999	S = 0.007867 Mollusca + 19.44 S = 0.01103 Coleoptera + 19.53 S = 0.198 Plecoptera + 21.61 S = 0.03996 Oligochaeta + 21.34	-0.400 -0.164 -0.191 -0.372
			Crustacea	0.067 0.990	18.700 0.999	S = 0.06662 Crustacea + 18.7	0.068
			Exogenous	0.102 0.915	22.290 1.000	S = 0.1021 Exogenous + 22.29	-0.324
			Total	0.003 0.969	20.230 0.999	S = 0.002573 Total + 20.23	-0.167
			Diptera	0.000 0.750	3.518	1.000	WG = 0.0004204 Diptera + 3.518	-0.236
			Trichoptera	0.001 0.618	3.567	1.000	WG = 0.0005287 Trichoptera + 3.567	-0.333
			Ephemeroptera	0.000 0.616	3.587	1.000	WG = 0.0002745 Ephemeroptera + 3.587	-0.326
	Average individual weight gain of alevins by reach WG	Abundance of invertebrates by reach Immediately after the flood	Mollusca Coleoptera Plecoptera Oligochaeta	0.000 0.838 0.000 0.663 0.001 0.537 0.000 0.554	3.226 3.477 3.610 3.589	1.000 1.000 1.000 1.000	WG = 0.0004618 Mollusca + 3.226 WG = 0.0002509 Coleoptera + 3.477 WG = 0.001053 Plecoptera + 3.61 WG = 0.0003731 Oligochaeta + 3.589	-0.108 -0.304 -0.344 -0.337
			Crustacea	0.000 0.511	3.628	1.000	WG = 0.00007267 Crustacea + 3.628	-0.346
			Exogenous	0.007 0.835	3.374	1.000	WG = 0.006682 Exogenous + 3.374	-0.121
			Total	0.000 0.738	3.417	1.000	WG = 0.00009113 Total + 3.417	-0.258
			Diptera	0.003 0.950	11.440 1.000	Prod = 0.003001 Diptera + 11.44	0.159
			Trichoptera	0.009 0.956	11.160 1.000	Prod = 0.008547 Trichoptera + 11.16	0.177
			Ephemeroptera	0.005 0.979	11.330 1.000	Prod = 0.00519 Ephemeroptera + 11.33	0.344
	Fish productivity by reach Prod	Abundance of invertebrates by reach Immediately after the flood	Mollusca Coleoptera Plecoptera Oligochaeta	0.002 0.942 0.003 0.964 0.044 0.886 0.007 0.801	10.320 1.000 10.360 1.000 11.220 1.000 11.380 1.000	Prod = 0.002216 Mollusca + 10.32 Prod = 0.003065 Coleoptera + 10.36 Prod = 0.04409 Plecoptera + 11.22 Prod = 0.007145 Oligochaeta + 11.38	0.088 0.204 -0.035 -0.173
			Crustacea	0.017 0.968	10.280 1.000	Prod = 0.01685 Crustacea + 10.28	0.224
			Exogenous	0.032 0.941	11.010 1.000	Prod = 0.03226 Exogenous + 11.01	0.106
			Total	0.001 0.973	10.410 1.000	Prod = 0.0007759 Total + 10.41	0.271

Table 4 .

 4 4 Correlations between invertebrate abundances at the end of the trial (X) and fish variable (Y), mean of posterior probability distribution functions of a (the slope coefficient) and b (the intercept) and fit of the models. Parameters were considered significant when P(X > 0) is less than 0.10 or above 0.90 (in bold).

	Fish variable (Y)	Invertebrate variable (X)	Groups	a	P(a > 0)	b	P(b > 0)	Equations	Fit
			Diptera	-0.001	0.213	27.850	1.000	S = -0.0008931 Diptera + 27.85	-0.046
			Trichoptera	0.037	0.954	22.060	1.000	S = 0.03713 Trichoptera + 22.06	-0.164
			Ephemeroptera 0.017	0.994	20.400	1.000	S = 0.01676 Ephemeroptera + 20.4	0.250
	Number of surviving alevins by reach S	Abundance of invertebrates by reach At the end of the trial (33 days)	Mollusca Coleoptera Plecoptera Oligochaeta	0.004 0.002 0.262 0.019	0.993 0.939 0.877 0.808	21.840 23.050 20.470 24.520	1.000 1.000 0.995 1.000	S = 0.004215 Mollusca + 21.84 S = 0.002463 Coleoptera + 23.05 S = 0.2616 Plecoptera + 20.47 S = 0.01855 Oligochaeta + 24.52	0.283 -0.172 -0.566 -0.339
			Crustacea	0.016	0.934	20.260	0.997	S = 0.01626 Crustacea + 20.26	-0.393
			Exogenous	0.017	0.756	23.820	0.999	S = 0.0173 Exogenous + 23.82	-0.475
			Total	0.001	0.897	21.930	0.999	S = 0.0008152 Total + 21.93	-0.415
	Average individual
	weight gain of alevins
	by reach
	WG

Table 4 .

 4 5 Diet composition. Number of prey (N), relative abundance (A) and frequency of occurrence in fish (F) in the control and in the impacted reaches.

		Control			Flood
		N	A (%)	F (%)	N	A (%)	F (%)
	Diptera
	Chironomidae	269	68.3	100	424	76.1	96.9
	Simuliidae	8	2.0	21.9	5	0.9	15.6
	Empididae	-	-	-	1	0.2	3.1
	Brachycera	-	-	-	1	0.2	3.1
	?	2	0.5	6.3	1	0.2	3.1
	Trichoptera
	Polycentropodidae 7	1.8	18.8	9	1.6	21.9
	Philopotanidae	-	-	-	1	0.2	3.1
	Hydropsychidae	1	0.3	3.1	3	0.5	9.4
	Psychomyiidae	-	-	-	1	0.2	3.1
	?	5	1.3	15.6	5	0.9	12.5
	Ephemeroptera
	Baetidae	69	17.5	87.5	71	12.7	71.9
	Heptageniidae	3	0.8	9.4	4	0.7	6.3
	Ephemerellidae	11	2.8	21.9	3	0.5	9.4
	Caenidae	1	0.3	3.1	-	-	-
	?	1	0.3	3.1	1	0.2	3.1
	Coleoptera
	Elmidae	1	0.3	3.1	2	0.4	6.3
	Amphipoda
	Gammaridae	2	0.5	6.3	3	0.5	6.3
	Isopoda
	Asellidae	3	0.8	9.4	-	-	-
	Oligochaeta	1	0.3	3.1	-	-	-
	Others
	Hydracaria	4	1.0	3.1	6	1.1	6.3
	Hemiptera	-	-	-	1	0.2	3.1
	Nematoda	-	-	-	5	0.9	3.1
	?	1	0.3	3.1	1	0.2	3.1
	Exogenous	5	1.3	15.6	9	1.6	25.0
	Total	394			557

Table 4 .

 4

6 Synthesis of the main results on the effects of the artificial flood on invertebrate abundances (total and Diptera), alevin performances (survival and growth) and alevin diet (number of prey eaten and number of Chironomids eaten) in late spring.

Remerciements

Acknowledgements

The authors declare no conflicts of interest. E. Arevalo benefits from a cross-border grant (Univ Pau & Pays Adour/UPV) and from Mérimée, a program funded by the French Embassy.

Experiments were carried out thanks to the IE ECP facilities and financially supported by CG64 and INRA Ecoserv Metaprogram. We wish to thank F. Gueraud, P. Coste, E. Huchet and J.

Rives for their help with the experimental set-up and invertebrate sampling, J. Labonne for his comments on the manuscript and J. Almany for correcting the English manuscript.

Acknowledgements

No competing interests declared. E. Arevalo benefits from a cross-border grant (Univ Pau & Pays Adour/UPV) and from the French Embassy mobility helping program (Mérimée).

Experiments were carried out thanks to the IE ECP facilities and financially supported by CG64 and INRA Ecoserv Metaprogram. F. Gueraud, P. Coste, E. Huchet, S. Glise, F. Lange, E.

Plagnes-Juan and J. Rives helped in the experimental setting and the laboratory experiment. We thank J. Almany for correcting the English manuscript.

Chapter III Consequences of Global Climate Change on the community of invertebrates, on the survival, growth and behaviour of first-feeding alevins at high density: flood simulation in a semi-natural environment

Groups

Immediately after the flood At the end of the trial α1 P(α1 > 0) β1 P(β1 > 0) γ1 P(γ 1 > 0) α2 P(α2 > 0) β2 P(β2 > 0) γ 2 P(γ 2 > 0)

Effect of the flood on alevins

The survival probability was significantly higher in the control than in the impacted cages during the first trial (early spring -0.609 vs 0.376 -P(α > 0) = 0.004 -Figure 3 and Table 5).

The opposite was observed during the second trial (late spring), even if the ratio between the two survival levels was much lower (0.813 vs 0.937 -P(γ > 0) = 0.995).

At the beginning of the experiment, alevins weighted on average 0.117 g (SD = 0.007) at the first trial (early spring) and 0.078 g (SD = 0.008) at the second trial (late spring). After 21 or 22 days of growth, alevins weighted on average 0.118 g (SD = 0.015) at the end of first trial and 0.133 g (SD = 0.052) at the end of the second trial. Individual weight gain was higher in the control than in the impacted cages for both trials (0.028 vs -0.018 and 1.221 vs 0.237 -P(α > 0) = 0.100 and P(γ > 0) = 0.000).

Finally, there was no significant difference in fish productivity, but it tended to be higher in the control cages than in the impacted ones at both trials (2.349 vs 1.384 and 4.510 vs 2.928 -P(α > 0) = 0.110 and P(γ > 0) = 0.294). Whatever the considered variable, alevins performances were lower at the first trial than at the second one (P(β > 0) = 0.984, 1.000 and 0.964 for survival, individual weight gain and fish productivity).

Supporting Information of the manuscript in preparation

Supporting information: Relationship between the invertebrate abundances and alevin performance

We assessed the strength of link between the mean abundances of invertebrates and (i) the survival, (ii) the average individual weight gain, and (iii) the fish productivity. We tested 60 relationships (10 invertebrate groups x 3 fish performance variables x 2 times, just after the disturbance and at the end of the trial). Correlations were tested with OpenBUGS®. If Yj was the fish variable of the j th cage, we assumed:

Where Yj is for each j cage, the number of survivors (Sj), the average individual weight gain (WGj[i]) or the fish productivity (Prod.Fishj), a is the slope coefficient, Abondj is the average of abundance of invertebrates by cage and b is the intercept. Fit of correlation was calculated by dividing the variability unexplained by the variability observed in the data set, as follows:

When the fit is between 0.5 and 1, the correlation is good. When the fit is between 0 and 0.5, the correlation is intermediate and when the fit is negative, there is no correlation.

Immediately after the flood, abundances of invertebrates fitted with fish performances in 23% of the cases (7/30, fit > 0.500 -Table S3). At the end of the trials, final invertebrate abundances fitted with fish performances in 63% of the cases (19/30, fit > 0.500 -Table S4). Usually positive, the links happen to be negative in five cases but only one link was significantly negative: between fish productivity and abundances of Diptera. Focusing on final Total abundance, and on Diptera and Ephemeroptera (the two groups among which are the main food items of newborn alevins), correlations with the weight gain of alevins were positive and significant (Fit = 0.888, 0.914 and 0.712 respectively -Figure S2). However, they were highly influenced by one point (Control cage in the second trial) characterized by abundances especially high. When this point was not taken into account, correlation was still significant for total abundance (Fit: 0.851; a = 0.0015; P(a > 0) = 0.995), but no more for Diptera and Ephemeroptera (Fit = -0.712 and -2.325, respectively).

Chapter IV Consequences of Global Climate Change on the community of invertebrates, on the survival and growth of first-feeding alevins at low density: flood simulation in a semi-natural environment 2002; [START_REF] Heggenes | Spatial niche variability for young Atlantic salmon (Salmo salar) and brown trout (S. trutta) in heterogeneous streams[END_REF]. Food supply was provided by invertebrate drift from the Lapitxuri brook and by the production of the channel itself. To recover fish from a previous experiment, an electrofishing was conducted in the totality of the channel on 14/04/2015. On 15/04/2015, a flood was simulated with a motor-pump in the 4 left-bank reaches (Figure 4.1). The motorpump was used at the extreme right point during 5 seconds, then the pump was displaced by 20 cm to the left and used again during 5 seconds. This procedure was repeated over the entire width of the impacted reach (7 times) and such transects were made every 50 cm to cover the entire length of each impacted reach. The use of the motor-pump increased the water velocity up to 92.2 cm s -1 (SD = 18.9) on average, ranging from 45 to 146 cm s -1 in the 50 cm in front of the outflow. After the flood simulation, the gravel bed was flattened again.

Fish productivity

For each j reach, the final weights were summed to obtain the fish production per reach (Prod.Fishj).

𝜇. 𝑃𝑟𝑜𝑑. 𝐹𝑖𝑠ℎ 𝑗 = 𝜇 + 𝛼 𝐹𝑙𝑜𝑜𝑑 𝑗 + 𝛽 𝑗

Where μ is the average fish production, α is a fixed-effect parameter for the impact of the flood and β is a random effect corresponding to each j reach.

Relationship between the community of invertebrate and alevins

We linked (i) the number of surviving alevins by reach, (ii) the average individual weight gain of alevins by reach and (iii) fish productivity by reach according to the average invertebrate abundance by reach immediately after the flood or at the end of the experiment (33 days after).

Then, 60 relationships were tested (10 invertebrate groups x 3 variables for alevin performances

x 2 times). Correlations were tested with OpenBUGS®. If Yj was the variable of the j th reach, we assume:

Where Yj is the number of survivors by reach (Sj), the fish weight gain by reach (WGj[i]) or the fish productivity by reach (Prod.Fishj), a is the slope coefficient, Abondj is the average invertebrate abundance by reach and b is the intercept.

Fit of the correlation is calculated by dividing the variability unexplained by the correlation model with the variability observed in the data set, as follows:

When the fit is negative, there is no correlation. When the fit is between 0 and 0.5, the correlation is intermediate. When the fit is between 0.5 and 1, the correlation is good.

Gut contents

For the description of the diet, data were pooled according to conditions (control vs. impacted).

The total number of prey (N), the relative abundance (A) and the occurrence of prey in fish (F, where the number of alevins containing this kind of prey is divided by the total number of alevins sampled) were calculated for each family.

quality of the correlations, on 12 relationships, the slope coefficient was significantly positive five times (P(a > 0) > 0.9) and significantly negative once (P(a > 0) < 0.1), leading to the following hypotheses: the initial abundance of Diptera and Ephemeroptera increased the survival and the productivity of fish (Table 4.3). However, the growth of alevins decreased the abundance of Ephemeroptera at the end of the trial (Table 4.4).

Gut contents

All the sixty-four alevins had prey in the stomach. The number of preys ingested was 1.5 lower in the control reaches when compared to the impacted ones (Table 4.5). The Chironomidae family was found in 100% of alevins from the control reaches and in 96.9% of alevins from the impacted ones. Chironomidae represented 68.3% and 76.1% of the prey consumed in the control and impacted reaches, respectively. Baetidae was the second most represented group (17.5% of prey in the control and 12.7% in the impacted reaches). The other groups represented less than 3% of the total amount of prey ingested.

Model estimations highlighted that fish consumed significantly less prey in the control (12.3 prey on average) than in the impacted reaches (17.4 prey on average, P(α > 0) = 0.998 -Figure 4.4). Accordingly, fish ate significantly less Chironomidae in the control (8.4 in average) than in the impacted reaches (13.2 in average, P(α > 0) = 993 -Figure 4.4). However, there was no difference in the number of Baetidae ingested (2.1 vs 2.2 respectively, in the control and in the impacted reaches -P(α > 0) = 0.559).

General discussion