Clémentine François

Laurent Simon

Florian Malard

Tristan Lefébure

Christophe Jean Douady

Florian Mermillod-Blondin

Clémentine M Francois
email: clementine.francois@univ-lyon1.fr

Trophic selectivity in aquatic isopods increases with the availability of resources

Keywords:

INTRODUCTION

Trophic selectivity has been recognized to have profound consequences for consumers' fitness [START_REF] Cruz-Rivera | Can quantity replace quality? Food choice, compensatory feeding, and fitness of marine mesograzers[END_REF]McMurray et al., 2016), for the stability of grazer and prey populations (Strom & Loukos, 1998;Meunier et al., 2016), and for nutrient fluxes and recycling in food webs (Marcarelli et al., 2011;Hood et al., 2014). Trophic selectivity occurs when the relative abundances of resource types in a consumer's diet differ from their relative abundances in the environment [START_REF] Chesson | Measuring preference in selective predation[END_REF]. Therefore, selective feeders are consumers discriminating among the available resources in the environment by selecting specific resources (i.e. resources which are more represented in consumer's diet than in the environment; [START_REF] Feinsinger | A simple measure of niche breadth[END_REF][START_REF] Feinsinger | A simple measure of niche breadth[END_REF][START_REF] Peterson | Diets and selective feeding by larvae of Atlantic mackerel Scomber scombrus on zooplankton. Marine ecology progress series[END_REF].

The degree of trophic selectivity in organisms has been predicted to vary with the availability of resources in the environment (e.g. Anthony & Kunz, 1977;[START_REF] Tinker | Food limitation leads to behavioral diversification and dietary specialization in sea otters[END_REF]Correa & Winemiller, 2014). According to the predictions of the optimal foraging theory (OFT; Roughgarden, 1972;Pyke et al., 1977;Krebs et al., 1997), an increase in resource availability for organisms feeding on a variety of resources should lead to an increased selectivity on their preferred resources. These OFT predictions have been experimentally demonstrated in rodents and wasps by [START_REF] Lacher | Food preference as a function of resource abundance with multiple prey types: an experimental analysis of optimal foraging theory[END_REF] and [START_REF] Armstrong | Effects of prey quantity on predatory wasps (Polistes dominulus) when patch quality differs[END_REF], respectively. While informative, these intra-species experimental tests were effectively bound to a narrow range of resource availability change (3-fold) and hardly represented the actual resources distribution in natural ecosystems (controlled laboratory experiment; choice between two artificially-contrasted food sources). Testing the relationship between resource availability and selectivity in a biologically realistic and relevant framework would thus require to study in situ a wide range of environmental trophic conditions, a condition hardly met without working at the species level. Although previous experimental validations of OFT predictions were only obtained at the individual and population levels, we can expect the same kind of relationship at the species level following the hypothesis developed by [START_REF] Evans | Species-energy relationships at the macroecological scale: a review of the mechanisms[END_REF]. These authors predicted that an increase in total resource availability would increase the abundance of rare resources that would then become available in sufficient quantity to enable organisms to selectively feed on them. Consequently, trophic selectivity of species would increase with resource availability in the field.

Trophic selectivity in animals has also been expected to vary with the qualities of resources present in their environment (e.g. [START_REF] Cruz-Rivera | Can quantity replace quality? Food choice, compensatory feeding, and fitness of marine mesograzers[END_REF][START_REF] Zandona | Diet quality and prey selectivity correlate with life histories and predation regime in Trinidadian guppies[END_REF]. Indeed, discriminating between the available resources would imply more benefit for the organism in an environment where resources span a wide range of nutritional qualities than in an environment where all resources have similar qualities. Many proxies of resource quality have been proposed, including the widely used C:N and C:P ratios [START_REF] Hessen | RNA responses to N-and Plimitation; reciprocal regulation of stoichiometry and growth rate in Brachionus[END_REF][START_REF] Hillebrand | Ecological stoichiometry of indirect grazer effects on periphyton nutrient content[END_REF].

According to the ecological stoichiometry theory [START_REF] Sterner | Ecological stoichiometry: the biology of elements from molecules to the biosphere[END_REF][START_REF] Elser | Biological stoichiometry: a chemical bridge between ecosystem ecology and evolutionary biology[END_REF][START_REF] Hessen | Ecological stoichiometry: an elementary approach using basic principles[END_REF]Hood et al., 2014), organisms would select their trophic resources to minimize the stoichiometric mismatch between their elemental composition and that of their consumed resources. They are expected to feed selectively on the resources whose C:N and/or C:P ratios are as close as possible to their own ratios to optimize their nutrient acquisition. This selective stoichiometric choice would be achievable only in environments with resources of differing qualities. As a consequence, trophic selectivity would increase with the diversity (i.e. heterogeneity) of available resources in terms of stoichiometric quality.

Although theory predicts that the availability as well as the heterogeneity in the quality of the resources influences trophic selectivity, in situ explicit tests of their respective effects on selective feeding are still lacking. The present paper aims to fill this gap by studying in situ the trophic selectivity among species living along a gradient of resource availability. In this context, four analytical challenges widely recognized in the literature must be addressed [START_REF] Evans | Species-energy relationships at the macroecological scale: a review of the mechanisms[END_REF].

First, a wide environmental gradient in resource availability along which many closely related species can be sampled is necessary. Indeed, earlier comparisons between two contrasted environments are instructive (e.g. [START_REF] Huntsman | Effects of organic matter availability on the life history and production of a top vertebrate predator (Plethodontidae: Gyrinophilus palleucus) in two cave streams[END_REF], but lack support for generalization.

Second, the quantification of resource availability along the studied gradients was often limited to coarse estimations using various databases and models without precise assessments of available resources in the field [START_REF] Gende | Consumption choice by bears feeding on salmon[END_REF][START_REF] Kaspari | Taxonomic level, trophic biology and the regulation of local abundance[END_REF][START_REF] Birkhofer | The global relationship between climate, net primary production and the diet of spiders[END_REF]. Third, accurate long-term diet estimation requires to integrate resource assimilation over a large period of time. However, estimations of species diet relying on gut content analyses, measurements of resource consumption or visual observations [START_REF] Lacher | Food preference as a function of resource abundance with multiple prey types: an experimental analysis of optimal foraging theory[END_REF][START_REF] Armstrong | Effects of prey quantity on predatory wasps (Polistes dominulus) when patch quality differs[END_REF][START_REF] Huntsman | Effects of organic matter availability on the life history and production of a top vertebrate predator (Plethodontidae: Gyrinophilus palleucus) in two cave streams[END_REF][START_REF] Zandona | Diet quality and prey selectivity correlate with life histories and predation regime in Trinidadian guppies[END_REF] provide a snapshot of the diet at a given time and do not consider differences in digestibility among resources (e.g. [START_REF] Trumble | Digestive constraints on an aquatic carnivore: effects of feeding frequency and prey composition on harbor seals[END_REF]. Fourth, most broad-scale studies did not account for the phylogenetic relatedness among species even though it is now acknowledged that most biological traits are phylogenetically conserved [START_REF] Felsenstein | Phylogenies and the comparative method[END_REF][START_REF] Blomberg | Testing for phylogenetic signal in comparative data: behavioral traits are more labile[END_REF][START_REF] Pienaar | Macroevolution of life-history traits in passerine birds: adaptation and phylogenetic inertia[END_REF].

In the present paper, we addressed the aforementioned challenges through a dedicated sampling design and the use of accurate analytical and statistical methods. We focused on aquatic isopod species of the family Asellidae which represent ideal biological models due to their distribution along a wide environmental gradient ranging from deep cave systems (among the most energylimited environments on Earth; [START_REF] Poulson | The trophic basis of subsurface ecosystems. Chapter 12 in[END_REF][START_REF] Gibert | Subterranean Ecosystems: A Truncated Functional Biodiversity[END_REF][START_REF] Venarsky | Quantitative food web analysis supports the energy-limitation hypothesis in cave stream ecosystems[END_REF] to highly-productive surface streams. Moreover, their phylogeny has been precisely described [START_REF] Morvan | Timetree of Aselloidea reveals species diversification dynamics in groundwater[END_REF], enabling to account for phylogenetic inertia. Based on a deep knowledge of their habitat and ecology [START_REF] Simon | Food web structure and the role of epilithic biofilms in cave streams[END_REF][START_REF] Leberfinger | The importance of terrestrial resource subsidies for shredders in open-canopy streams revealed by stable isotope analysis[END_REF][START_REF] Mermillod-Blondin | Thermal tolerance breadths among groundwater crustaceans living in a thermally constant environment[END_REF][START_REF] Eme | Integrating phylogeography, physiology and habitat modelling to explore species range determinants[END_REF]Francois et al., 2016a), a standardized sampling design was built to evaluate the abundance as well as the stoichiometric quality (based on C:N and C:P ratios) of all available resources in diverse environments. This precise characterization of trophic resources was combined with diet determination to measure the degree of selectivity of each species. Isopod diets were determined by analyzing the C and N stable isotope ratios of individuals and their resources. The isotopic composition of an individual is a long-term integrator of its trophic choices and reflects what has been actually assimilated in its tissues, summarizing ingestion and digestibility of each resource [START_REF] Peterson | Stable isotopes in ecosystem studies[END_REF][START_REF] Fry | δ 13 C measurements as indicators of carbon flow in marine and freshwater ecosystems[END_REF][START_REF] Traugott | Empirically characterising trophic networks: what emerging DNA-based methods, stable isotope and fatty acid analyses can offer[END_REF].

Based on 15 closely related species of Asellidae, we obtained an original dataset enabling to test two predictions along one of the largest gradient of resource availability existing in natural ecosystems. First, trophic selectivity of aquatic asellids should increase with resource availability following the prediction of [START_REF] Evans | Species-energy relationships at the macroecological scale: a review of the mechanisms[END_REF]. Second, trophic selectivity should increase with the diversity in stoichiometric quality of resources according to the ecological stoichiometric theory.

MATERIALS AND METHODS

Biological model

Our data set comprised 15 species of Asellidae collected in environments spanning a wide range (from caves to surface streams) of freshwater habitats in Southern Europe (Table 1). Species of isopods belonging to this family are known to feed on a wide range of food resources, including coarse and fine particulate organic matter (CPOM and FPOM), filamentous algae, aquatic macrophytes, epilithic biofilm and periphyton [START_REF] Moore | The role of algae in the diet of Asellus aquaticus L. and Gammarus pulex L[END_REF][START_REF] Marcus | Feeding and growth of Asellus aquaticus (Isopoda) on food items from the littoral of Windermere, including green leaves of Elodea canadensis[END_REF][START_REF] Rossi | Role of fungi in the trophic niche of the congeneric detritivorous Asellus aquaticus and A. coxalis (Isopoda)[END_REF][START_REF] Basset | Relationships between trophic niche breadth and reproductive capabilities in a population of Proasellus coxalis Dollfus (Crustacea: Isopoda)[END_REF][START_REF] Arakelova | Feeding strategy and energy allocation in Asellus aquaticus L[END_REF][START_REF] Leberfinger | The importance of terrestrial resource subsidies for shredders in open-canopy streams revealed by stable isotope analysis[END_REF]Francois et al., 2016a).

Given this diversity of food resources, we used a commonly accepted procedure which consists in discriminating between broad food compartments [START_REF] Simon | Food web structure and the role of epilithic biofilms in cave streams[END_REF][START_REF] Leberfinger | The importance of terrestrial resource subsidies for shredders in open-canopy streams revealed by stable isotope analysis[END_REF].

These compartments correspond to coarse particulate organic matter (CPOM; 1 mm < particle size < 6 mm), fine particulate organic matter (FPOM; particle size < 1 mm), roots, algae, sedimentary biofilm developed on sand particles (200 µm < particle size < 1000 µm), biofilm attached to the debris of animal cuticle (epicuticular biofilm) and thick biofilm attached to submerged rocks (epilithic periphyton).

Sampling

The 15 species were sampled from July to October 2011 in caves, springs and streams. Only one focal asellid species was present in each sampling site. For each species, from 9 to 18 individuals were collected (at sight or using the Bou-Rouch method [START_REF] Bou | Methods to sample in interstitial groundwaters (Les méthodes de récolte dans les eaux souterraines interstitielles)[END_REF] or a Surber sampler), except for the species P. parvulus with only 4 sampled individuals (see Table S1 in Supporting Information). In parallel, the trophic resources known to be edible by Asellidae were sampled in each environment. This sampling consisted in 3 replicates per environment, in order to be representative of the spatial heterogeneity. Following the approach of [START_REF] Huntsman | Effects of organic matter availability on the life history and production of a top vertebrate predator (Plethodontidae: Gyrinophilus palleucus) in two cave streams[END_REF], each replicated sampling consisted in collecting on a depth of 1 cm all available resources occurring on a standardized area (256 cm²). After sampling, both samples of trophic resources and isopods were flash-frozen with liquid nitrogen in the field for conservation during the journey back to the laboratory.

Trophic resources in the environment

At the laboratory, all collected trophic resources were unfrozen and sorted according to the food categories described above by using nets of different mesh sizes (200 µm, 1 mm and 6 mm). Food categories were then frozen, freeze-dried and weighted to obtain a dry mass of each trophic resource per square meter (g of Dry Weight (DW) / m²) for each site (Table S2). All obtained resource samples were then ground using a ball mill grinder (Retsch MM-200) for homogenization before the determination of resource elemental composition (%OC, %N and %P, i.e. the organic carbon (OC), nitrogen and available phosphorus content of each resource, as % of dry mass; Table S2). For sedimentary biofilm and periphyton, samples were treated with 1N HCl before OC analysis to remove carbonates using the 'capsule method', as described in [START_REF] Brodie | Evidence for bias in C and N concentrations and δ13C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF]. Elemental composition was then measured alongside the determination of isotopic ratios (see below), allowing to calculate the C:N stoichiometric molar ratio of each resource (Table S2).

The quantity of each resource (g of DW / m²) and its elemental composition (%OC) enabled to calculate the OC content for each resource per site (g of OC sequestered in a given resource . m -2 ).

Phosphorus content of isopods and trophic resources

Chemical analyses were performed to determine the phosphorus content of isopods and of their trophic resources (% of P per dry mass). For isopods and trophic resources (except sedimentary biofilm and periphyton), P content was determined on ground samples digested in an autoclave for 30 minutes at 121°C and 15-20 psi with potassium persulfate and sulfuric acid to convert all phosphorus to orthophosphate. Then, orthophosphate content of each sample was determined by the molybdate/ascorbic acid blue method [START_REF] Murphy | A single-solution method for the determination of soluble phosphate in sea water[END_REF]. For sedimentary biofilm and periphyton, as all fractions of total phosphorus are not available for the biota [START_REF] Golterman | Fractionation and bioavailability of phosphates in lacustrine sediments: a review[END_REF], the quantity of P available for asellids was determined by measuring the available organic P.

Following [START_REF] Ni | Characteristics of bioavailable organic phosphorus in sediment and its contribution to lake eutrophication in China[END_REF], available organic P was extracted from resource samples (n = 3 per resource type) with NaOH 1M. After extraction during 16 hours, the supernatant was collected, its pH was stabilized with HCl (3.5M) and the extracted P that has been converted in orthophosphate was quantified using the molybdate/ascorbic acid blue method [START_REF] Murphy | A single-solution method for the determination of soluble phosphate in sea water[END_REF].

Stable isotope measurements

C and N stable isotope ratios were measured for all isopods (previously freeze-dried) and freezedried resource samples, jointly with the measure of their elemental composition (%OC, %N). This joint analysis was not possible for sedimentary biofilm and periphyton samples which were previously treated for carbonate removal using HCl ('capsule method'). For these samples, we performed separate analyses to avoid any damage of the isotope ratio mass spectrometer with chloride. According to Francois et al. (2016a), the 'rinse method' [START_REF] Brodie | Evidence for bias in C and N concentrations and δ13C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods[END_REF] was used for chloride removal before measurement of isotopic ratios. Elemental composition and stable isotope ratios of C and N were measured using an isotope ratio mass spectrometer (Isoprime100, Isoprime Ltd, Manchester, UK) coupled in continuous flow with an elemental analyzer (Thermo FlashEA 1112, ThermoElectro, Milan, Italy). In-house standards calibrated against IAEA-N1, IAEA-N2, IAEA-CH6 and IAEA-C3 reference materials were analyzed with the samples, and standard deviations of replicate analyses were lower than 0.20 ‰. C and N stable isotope compositions were expressed as δ in ‰ with V-PDB (δ 13 C) and Air (δ 15 N) as standards.

Diet determination

The diets of the 15 species were determined by analyzing the C and N stable isotope signatures of isopods and their available resources with the Bayesian mixing model SIAR [START_REF] Parnell | Source partitioning using stable isotopes: coping with too much variation[END_REF].

The model was run with 1 million of iterations, 50% of burn-in and a thinning of 250. As the diettissue discrimination has not been determined for isopod species, we used the commonly accepted values of 2.54 ± 1.27 and 0.4 ± 1.3 for nitrogen (Δ15N) and carbon isotope discrimination (Δ13C), respectively (Vander Zanden & Rasmussen, 2001;[START_REF] Post | Using stable isotopes to estimate trophic position: models, methods, and assumptions[END_REF][START_REF] Vanderklift | Sources of variation in consumer-diet δ 15 N enrichment: a meta-analysis[END_REF]. The choice of discrimination factors for non-model organisms is tricky and may impact the results of the mixing model. The isoplots (Fig. S1) suggest that the value chosen for Δ15N may be underestimated for some groundwater species, as some individuals fall outside the source polygon in terms of δ 15 N (exceeding by up to 2‰ the expected values) in four species (P. arthrodilus, B. molinai, P. spelaeus and P. parvulus). Although a higher Δ15N would have corrected the discrepancies in isoplots for these four species, it would not have significantly changed diet estimations for these species. Thus, the commonly accepted value of 2.54 ± 1.27 for Δ15N was used to be consistent in the analysis of the 15 species. Posterior probability distributions of the SIAR models were checked for unimodality. Median and 95% credibility interval were reported for each resource contributing to the diet (Table S3).

To determine the sample size of consumers collected in each environment, we followed the guidelines of [START_REF] Jackson | Comparing isotopic niche widths among and within communities: SIBER-Stable Isotope Bayesian Ellipses in R[END_REF] who recommend a minimum of 10 individuals to properly quantify the species trophic niche, at the exception of P. parvulus for which only 4 individuals could be collected. For this species, the small sample size implied a higher uncertainty in the diet reconstruction, and a potential underestimation of trophic niche size.

Including too many resources in Bayesian mixing models can lead to confounding results (e.g. inconsistent or bimodal posterior distributions). In this case, [START_REF] Hopkins | Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model[END_REF] and [START_REF] Phillips | Best practices for use of stable isotope mixing models in food-web studies[END_REF] advised to combine biologically related resources having similar isotopic signatures to increase the predictive power of the model. Thus, FPOM and CPOM were combined in a unique resource (hereafter 'FPOM + CPOM') when more than 3 resources were considered in the diet of isopods and when the average δ 13 C and δ 15 N of FPOM and CPOM differed by no more than 1.3‰. This resource combination successfully improved the diet determination for 2 species: P. coiffaiti and P. ibericus. For one species (P. coiffaiti), we measured differences between the C isotopic signatures of resources (algae) and consumers that were not consistent with the discrimination factor (Δ13C = 0.4 ± 1.3). This discrepancy did not influence our results as discussed in the Supporting Note S1. It is worth noting that the bayesian mixing models used for diet reconstruction usually generate large confidence intervals, as they integrate all biological uncertainties. If the bayesian mixing model does not properly converge, results are biased towards the null generalist assumption, i.e. a diet composed of n resources in proportion 1/n [START_REF] Brett | Resource polygon geometry predicts Bayesian stable isotope mixing model bias[END_REF]).

In our dataset, only 3 out of the 15 reconstructed diets display a pattern close to this equiproportional null scenario (P. arthrodilus, P. granadensis and to a lesser extent B. peltatus), which may indicate uncertainty in diet reconstruction. The 15 isoplots are displayed in the Fig. S1.

Estimated variables and data analyses

Standardization of the availability of trophic resources

Trophic resources had contrasted elemental composition (e.g. the %OC ranged from 0.06% for sedimentary biofilm per mass of dry sediment to 47% for roots). Thus, the absolute quantity of a given resource (in g of DW / m²) is not representative of its energetic significance for a consumer.

The OC content sequestered in a given resource provides a much more accurate estimation of this significance and thus the availability of each trophic resource was expressed as amount of OC . m - 2 .

Indices of trophic selectivity

Several indices have been proposed in the literature to measure the degree of selectivity of a consumer [START_REF] Hurlbert | The measurement of niche overlap and some relatives[END_REF][START_REF] Feinsinger | A simple measure of niche breadth[END_REF][START_REF] Smith | Niche breadth, resource availability, and inference[END_REF]. They all rely on a comparison between the proportions of resources in the environment (i.e. here their relative abundances in terms of OC content) and their respective proportions in the consumer diet. If these environment and diet proportions match closely, this consumer is feeding non-selectively on all available resources. If there is a strong mismatch between these proportions (i.e. between the availability and the use of resources), this consumer is feeding highly selectively.

We evaluated the degree of selectivity of the 15 studied species by calculating the Proportional Similarity index (PS) proposed by [START_REF] Feinsinger | A simple measure of niche breadth[END_REF]:

PS = 1 -0.5 * ∑ | qi -pi | (1)
where pi stands for the availability of the resource i in the environment (i.e. the relative abundance in terms of OC content) and qi stands for the dietary use of this resource i (i.e. the proportion in the diet, in terms of C and N atoms assimilated in the consumer tissues). This index measures the similarity between the distribution of resource availability and resource use. To highlight the dissimilarity between these distributions (i.e. the selectivity), we modified PS to PS':

Trophic selectivity: PS' = 1 -PS (2)
The obtained index PS' thus actually reflects the degree of consumer selectivity and ranges from 0 (non-selective feeding) to 1 (highly selective feeding). Two other selectivity indices were proposed in the literature: the index B of [START_REF] Hurlbert | The measurement of niche overlap and some relatives[END_REF] and the index FT of [START_REF] Smith | Niche breadth, resource availability, and inference[END_REF]. These two indices (described in the Supporting Methods S1 and Table S1) were highly correlated with the PS index (Spearman correlation coefficients ρ = 0.95 and 0.98 for B and FT respectively).

Thus, we only presented the results obtained with the index PS'. To consider the uncertainty inherent to Bayesian mixing models, for each species 1,000 diet proportion vectors (i.e. [p1; p2;…; pn] for n resources contributing to the diet) were sampled from the posterior distributions output by SIAR and subsequently used to draw a distribution of PS' values and calculate the 95% credibility interval for PS'.

Resource availability per site

Based on our OC quantifications, the resource availability (RA) in each sampling site was estimated by the mass of OC per surface unit (g of OC . m -2 ), summed over all collected resources edible by asellids (an approach similar to [START_REF] Srivastava | Why more productive sites have more species: an experimental test of theory using tree-hole communities[END_REF][START_REF] Thompson | Energy availability, spatial heterogeneity and ecosystem size predict food-web structure in streams[END_REF].

Quality of resources

The quality of a given resource was estimated by the strength of the elemental imbalance between this resource and the consumer according to the theory of ecological stoichiometry [START_REF] Sterner | Ecological stoichiometry: the biology of elements from molecules to the biosphere[END_REF][START_REF] Frost | Are you what you eat? Physiological constraints on organismal stoichiometry in an elementally imbalanced world[END_REF][START_REF] Hessen | Ecological stoichiometry: an elementary approach using basic principles[END_REF]. The stronger is this elemental imbalance, the less beneficial it is for the consumer to feed on this resource. For a given resource i, this stoichiometric mismatch (SMi(C:N) or SMi(C:P)) was measured by the difference between the C:N (or C:P) ratio of the resource i and the C:N (or C:P) ratio of the consumer [START_REF] Elser | A stoichiometric analysis of the zooplankton-phytoplankton interaction in marine and freshwater ecosystems[END_REF][START_REF] Sterner | Ecological stoichiometry: the biology of elements from molecules to the biosphere[END_REF] (see Table 1 for the C:N and C:P of isopods and Table S2 for where µ* is the weighted mean of the SM of all resources weighted by their relative abundance pi.

As the total number of available resources (RR; Resource Richness) differed between sites, this weighted variance was corrected for sample size and transformed in a weighted standard deviation:

Resource Diversity in terms of Quality: RDQual(C:N or C:P)= sqrt ( σ² / ( (RR -1 ) / RR ) ) (5)

This index measures the heterogeneity in the stoichiometric mismatches of all available resources in the environment. Low RDQual(C:N or C:P) indicates that all available resources in the environment have similar stoichiometric qualities for the consumer. On the opposite, RDQual(C:N or C:P) increases when the stoichiometric qualities for the consumer increasingly diverge among available resources and when these resources having different stoichiometric qualities are available in even proportions (this latter property is similar to an evenness index which is maximal when all resources are in even proportions and minimal when one resource is predominant). According to our prediction, trophic selectivity would increase when RDQual(C:N or C:P) increases.

Number of trophic resources

In addition to Resource Availability (RA) and Resource Diversity in terms of Quality (RDQual(C:N or C:P)), the number of trophic resources present in each environment can also influence trophic selectivity by a purely probabilistic effect. Indeed, an increase in the number of resources enhances the trophic choice and influences the probability of selective feeding by organisms, independently of resources quality. To include this probabilistic hypothesis in our data analyses, the Resource Diversity in terms of Quantity (RDQuant, Table S1) was estimated in each environment with a Shannon index that integrates resource number and evenness (relative abundance) across all available resources:

Resource Diversity in terms of Quantity: RDQuant = -∑ pi . ln(pi)

where pi is the relative abundance of the resource i in the environment (relative to the total amount of OC).

Phylogenetic analyses

Evaluating the relative influences of the Resource Availability (RA) and the Resource Diversity in terms of Quality (RDQual(C:N or C:P)) and in terms of Quantity (RDQuant) on trophic selectivity needs to take into account the phylogenetic relationships among the 15 species. Taking advantages of whole transcriptome sequencing for the 15 species considered in the present study (Francois et al., 2016b), we extracted a set of 386 1-to-1 orthologous gene alignments present in all species. The concatenated alignment was used to reconstruct a phylogram with phyML with a GTR+G+I model of evolution [START_REF] Guindon | New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0[END_REF]. Using this topology, we estimated a chronogram with mcmctree (Yang 2007) using default parameters, two independent runs to check chains convergences and setting the divergence between the Bragasellus and Proasellus to be not older than 150 MYA.

Statistical analyses

According to the recommendations of [START_REF] Warton | The arcsine is asinine: the analysis of proportions in ecology[END_REF] for proportion data, the trophic selectivity index PS' has been logit-transformed prior to statistical analyses. RA and RDQualC:P have been log-transformed prior to statistical analyses to correct for non-normality of the data. We checked for multi-collinearity among the four environmental variables (RA, RDQualC:N, RDQualC:P and RDQuant) using variance inflation factors (VIF) and found them to be in acceptable range (all VIF < 2.4; see [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF]. We used Phylogenetic Generalized Least-Squares models (PGLS; [START_REF] Martins | Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data[END_REF] to evaluate the influence of the four environmental variables on trophic selectivity while accounting for the phylogenetic relationships among the focal species (using the R package caper). Blomberg's K was estimated using the function phylosig in phytools R package [START_REF] Revell | phytools: an R package for phylogenetic comparative biology (and other things)[END_REF]. As a dataset based on 15 asellid species did not enable to statistically test the influence of four environmental variables and their interactions on trophic selectivity, we decided to perform two separate analyses with RA, RDQualC:N and RDQuant as explaining variables in one hand and RA, RDQualC:P and RDQuant as explaining variables in another hand. For each analysis, we performed a procedure of model selection based on information theory [START_REF] Burnham | Formal inference from more than one model: multimodel inference (MMI). Model Sel. Multimodel inference. A practical information-theoretic approach[END_REF] and the ranking of models according to Akaike's Information Criterion corrected for small sample size (AICc, MuMIn R package), starting from the most comprehensive model (including all interactions). We checked that the order of entry of each variable into the PGLS models did not change significantly the results. The adjusted McFadden's pseudo-R 2 [START_REF] Mcfadden | Conditional logit analysis of qualitative choice behavior[END_REF] was computed for all tested models to provide for a comparable estimation of the amount of explained variance among models with different number of parameters. The normality of the residuals of the final 'best' model (with the smallest AICc) was checked.

All data analyses were performed with R 3.4 software (R Development Core Team 2016).

RESULTS

Trophic resources, isopod diets and trophic selectivity

Our dataset comprised 15 environments characterized by a wide range of resource availability (RA) with amounts of OC . m -2 varying from 1.3 g to 57.6 g (Fig. 1, Tables S1 &S2). The Resource Diversity in terms of stoichiometric Quality was also contrasted among environments with RDQualC:N indexes ranging between 1.1 and 20.1, and RDQualC:P indexes ranging between 4.0 and 633.4 (Table S1). There was no collinearity between the four predictor variables RA, RDQualC:N,RDQualC:P and RDQuant (VIF = 1.65, 1.60, 2.44 and 1.49 respectively). Seven trophic resources were collected and quantified in the environments. Three resources (epicuticular biofilm, periphyton and algae) were found in only one or two environments whereas three resources (fine and coarse particulate organic matter, and sedimentary biofilm) were present in almost all environments (Table 2, Fig. 2). Each trophic resource was dominant (> 50%) in at least one environment, at the exception of periphyton and algae (Table 2).

Isotopic measurements and analyses using Bayesian mixing models showed that all available trophic resources were found in the diets of at least one asellid species (Fig. 2, Table 2). For the three resources found in almost all environments (fine and coarse particulate organic matter, and sedimentary biofilm), their contributions to animal diets widely varied across species (Fig. 2). For example, the sedimentary biofilm could be minor (< 10 %) or dominant (> 50 %) in the diets of asellid species.

The PS' trophic selectivity indexes based on the match between isopod diets and resource availabilities ranged from 0.042 to 0.919 (Fig. 1, Table S1). The dataset thus comprised nonselective feeding (PS' close to 0) and highly selective feeding (PS' close to 1) species of isopods.

Relationships between trophic selectivity and environmental variables

The first procedure of model selection starting from the comprehensive model including resource availability (RA), resource diversity in terms of quantity (RDQuant) and in terms of quality based on C:N ratios (RDQualC:N) and all interactions as predictors of trophic selectivity (Table 3) resulted in the selection of a model with the amount of available resources (RA) as unique predictor of trophic selectivity (PS') (PGLS; P-value = 0.0031 and R 2 = 0.46; equivalent to Fig. 3a). A comparable result was obtained when resource diversity in terms of quality based on C:P ratios (RDQualC:P) replaced RDQualC:N as predictor of trophic selectivity (Table S4). PS' was positively correlated with RA (Fig. 3a). PS' was not correlated with the resource diversity in terms of quality for C:N (RDQualC:N, Fig. 3c) or for C:P (RDQualC:P, Fig. 3d), neither with the resource diversity in terms of quantity (RDQuant, Fig. 3b). Trophic selectivity showed no phylogenetic signal among the 15 species (Blomberg's K = 0.09, P-value = 0.94; [START_REF] Blomberg | Testing for phylogenetic signal in comparative data: behavioral traits are more labile[END_REF]).

Among the 15 species studied, we found that 4 out of the 5 species living in environments with more than 25 g of OC . m -2 were selective-feeders (i.e. with a PS'>0.5, Fig. 1): P. solanasi, P. beticus, P. ibericus and B. peltatus. These species always fed selectively on resource types that were not dominant in the environment. For example, the diet of P. beticus comprised 56% of sedimentary biofilm whereas this resource type represented less than 2% of OC resources in the environment (Fig. 2). There was a tendency across species for feeding selectively on sedimentary biofilm but other resource types were also selected. If P. solanasi and P. beticus selected solely sedimentary biofilm in the environment, the two other selective-feeding species fed selectively on 2 or more resources: sedimentary biofilm and roots for P. ibericus, and CPOM, FPOM and sedimentary biofilm for B. peltatus.

DISCUSSION

The dataset collected for the present study allowed to test the influence of trophic environmental conditions (resource availability and diversity) on the trophic selectivity of aquatic speciesacross a broad range of environments. In this dataset, the absence of collinearity between the predictor variables allowed to discriminate the relative influences of trophic conditions on selectivity.

Coupling an accurate quantification of trophic resources in a wide range of environments with a fine determination of animal diets using stable isotopes showed that the amount of available resources rather than the diversity of resource (in terms of quality and quantity, respectively RDQual(C:N or C:P) and RDQuant) increases trophic selectivity of aquatic isopod species. The positive relationship between resource availability and trophic selectivity was probably uncovered in this study thanks to the wide range of resource availability studied (from 1.3 to 57.6 g of OC .

m -2 ) which incorporated both surface and cave streams, these latter being considered as one of the most energy-limited environments on Earth [START_REF] Poulson | The trophic basis of subsurface ecosystems. Chapter 12 in[END_REF][START_REF] Simon | Food web structure and the role of epilithic biofilms in cave streams[END_REF][START_REF] Venarsky | Effects of organic matter and season on leaf litter colonisation and breakdown in cave streams[END_REF][START_REF] Venarsky | Quantitative food web analysis supports the energy-limitation hypothesis in cave stream ecosystems[END_REF]. By contrasting environmental conditions, we demonstrated that species tend to feed non-selectively at lower bounds of resource availability whereas selectivefeeding species were detected in resource-rich environments. Intraspecific niche partitioning (sensu [START_REF] Bolnick | Measuring individual-level resource specialization[END_REF] could theoretically affect this pattern, as non-selective species can be composed of individuals feeding selectively on different resources. However, non-selective species at the lower bound of resource availability do not display particularly dispersed individuals (see the isoplots in Fig S1 ; except for B.molinai), neither do they display distinct groups of individuals feeding on (i.e. isotopically close to) different resources, suggesting that the observed selectivity pattern should be similar at the species and individual levels. The analysis also suggested that trophic selectivity was not phylogenetically conserved among the focal species.

One major finding of the present study is the demonstration of a positive relationship between resource availability and trophic selectivity in aquatic isopods. This relationship supports the hypothesis that trophic selectivity was low in resource-poor environments probably because species cannot fulfill their energy requirements by foraging on a reduced set of the available resources [START_REF] Thompson | Prey choice by marten during a decline in prey abundance[END_REF][START_REF] Tinker | Food limitation leads to behavioral diversification and dietary specialization in sea otters[END_REF]. Nevertheless, the mechanisms explaining selective feeding by isopods in resource-rich environments remain elusive. Our results did not validate the mechanism proposed by [START_REF] Evans | Species-energy relationships at the macroecological scale: a review of the mechanisms[END_REF] to explain the positive relationship between resource availability and trophic selectivity. These authors hypothesized that an increase in total resource availability could increase some rare resources which would then become available in sufficient quantity for species to selectively feed on them. However, sedimentary biofilm was selected by many selective-feeding isopods in resource-rich environments even when the amount of this trophic resource was lower than in resource-poor environments (eg. see P. ibericus, Fig. 2). These results did not give a strong support to the mechanism presented by [START_REF] Evans | Species-energy relationships at the macroecological scale: a review of the mechanisms[END_REF] to explain the positive relationship between resource availability and trophic selectivity. The mechanisms leading to increased trophic selectivity with increasing resource availability might also have been associated with other ecological factors. For instance, the species-energy theory indicates that species richness increases with resource availability in the environment (Bonn et al., 2004). In such condition, interactions among species would be stronger in resource-rich than in resource-poor environments, which could increase niche partitioning among species (MacArthur & Levins, 1967). This niche partitioning for food and/or habitat would therefore enhance the mismatch between the relative abundances of resources in species diet and their relative abundances in the environment (i.e. leading to an increased trophic selectivity). For example, the microhabitat partitioning reported for marine amphipods to avoid competitive exclusion and predation [START_REF] Cruz-Rivera | Can quantity replace quality? Food choice, compensatory feeding, and fitness of marine mesograzers[END_REF][START_REF] Best | Phenotypic and phylogenetic evidence for the role of food and habitat in the assembly of communities of marine amphipods[END_REF] indicates that each amphipod species does not have access to the whole range of resources present in the environment. We can expect the same mechanism to occur in our experiment for isopod species living in resource-rich environments where biotic interactions are expected to be strong. Demonstrating this mechanism will represent a significant advance in our understanding of ecosystem structure and functioning, but represents a difficult task as it requires to finely describe the spatial distributions of all species in each environment but also the biotic interactions among these species.

Our results demonstrated that trophic selectivity was associated with resource availability but not with the diversity (i.e. heterogeneity) in resource quality. We expected that the stoichiometric constraints would have determined the resource used by consumers in accordance with the stoichiometric ecological theory [START_REF] Sterner | Ecological stoichiometry: the biology of elements from molecules to the biosphere[END_REF]. In other words, a high diversity of resource stoichiometric qualities would have generated selective feeding whereas non-selective feeding was expected in environments harboring resources of comparable stoichiometric qualities.

These predictions were not validated because asellid species fed selectively or non-selectively regardless of the diversity in resource quality in their environment. For example, three species of selective feeders (PS' > 0.5) were observed in environments displaying comparatively low diversity in resource quality for C:N (RDQualC:N < 5 in our dataset, figure 3b), whereas no selective feeding was expected under the stoichiometric hypothesis in these environments.

Admittedly, some criticisms can be drawn concerning the use of stoichiometric C:N or C:P ratios to evaluate resource quality. Measuring C:N and C:P ratios of a given trophic resource as a 'bulk' can skew our analyses, as detritivores can selectively ingest and/or assimile only a subfraction of the considered pool (e.g. the conditioned leaves colonized by fungi; [START_REF] Constantini | Role of fungal patchiness on vegetal detritus in the trophic interactions between two brackish detritivores, Idotea baltica and Gammarus insensibilis[END_REF].

Besides, detrimental chemicals such as phenolic compounds may reduce the palatability of trophic resources without affecting their C:N and C:P ratios [START_REF] Oates | Food selection by the south Indian leafmonkey, Presbytis johnii, in relation to leaf chemistry[END_REF][START_REF] Dorenbosch | Herbivory in omnivorous fishes: effect of plant secondary metabolites and prey stoichiometry[END_REF] and compounds which cannot be synthetized by animals such as sterols or essential fatty acids may influence resource selectivity in a different way than C:N and C:P ratios [START_REF] Wagner | Nutritional indicators and their uses in ecology[END_REF][START_REF] Sperfeld | Bridging ecological stoichiometry and nutritional geometry with homeostasis concepts and integrative models of organism nutrition[END_REF]. However, our results gave support to the idea of a dietary mixing strategy in asellids because most selective-feeding species (eg. P. ibericus and B. peltatus) selected at least 2 trophic resources that were largely contrasted in terms of C:N and C:P (e.g. averaged C:N : 10.2 ± 4.2 for sedimentary biofilms, 21.4 ± 8.2 for FPOM, 22.4 ± 11.8 for roots, and 29.3 ± 18.9 for CPOM; Table S2). This suggests that asellid species need to mix trophic resources for nutrient acquisition (nutrient complementation hypothesis; [START_REF] Hägele | Dietary mixing in three generalist herbivores: nutrient complementation or toxin dilution?[END_REF]. Thus, we can assume that trophic selectivity in aquatic isopod species was not driven by the stoichiometric diversity of available resources because of the need for essential compounds that were not contained in the trophic resources having the lowest C:N or C:P mismatches with organisms (ie. best resources in terms of stoichiometric quality). To address this topic, use of specific markers such as amino acids and fatty acids and their isotopic compositions would be pertinent to have more precise reconstructions of asellid diets and more precise quantifications of the diversity of trophic resources in the environment than those obtained from bulk analyses of N and C [START_REF] Bec | Assessing the reliability of fatty acid-specific stable isotope analysis for trophic studies[END_REF].

Although we observed that asellid species could selectively feed on several trophic resources, sedimentary biofilm was generally found in higher proportion in the diet of selective-feeding isopods than in their environment. Besides, a previous study by Francois et al. (2016a) demonstrated that two asellid species exhibited a strong specialization (i.e. a higher performance; [START_REF] Irschick | Phylogenetic methodologies for studying specialization[END_REF][START_REF] Whitfield | Diet specificity is not associated with increased reproductive performance of Golden Eagles Aquila chrysaetos in Western Scotland[END_REF][START_REF] Devictor | Defining and measuring ecological specialization[END_REF][START_REF] Poisot | A conceptual framework for the evolution of ecological specialisation[END_REF] on sedimentary biofilm. Feeding experiments in the laboratory showed that both species assimilated about 10 times more carbon and at least 4 times more nitrogen from sedimentary biofilm than from coarse and fine particulate organic matter. Thus, the fact that selective feeding on sedimentary biofilm was common in the present study might have resulted from a positive fitness consequence for isopods to use this resource. Nevertheless, we cannot conclude from the present experiment that selective feeding on sedimentary biofilm conferred a better performance to asellid species than feeding on other resources without measurements of the fitness of all isopod species when feeding on individual resources present in their environments. Coupling our original dataset on trophic selectivity of asellid species with measures of their performance on the available resources would provide a unique opportunity to document and understand the relationship between trophic specialization and selectivity in regard to resource availability. For each site, the upper panel presents the relative abundance of all available resources in the environment (in %; see Methods for details on the underlying calculations), while the lower panel presents the contribution of these resources to the diet of isopods (in %).

FIGURES CAPTION

For a given environment, the total resource availability (indicated on the left hand side of the barplots, in grams of Organic Carbon per square meter) is summed over all available resources.

Environments (and their corresponding species) are sorted by increasing resource availability. These plots present biological data prior to transformation. Only significant regression lines are drawn. See M&M for details on the credibility intervals drawn for PS'. Species are sorted by increasing resource availability in their environment. C:N org and C:P org refers respectively to the molar carbon-to-nitrogen and carbon-to-phosphorus ratios of isopods (whole organism). CO stands for the ISO country code and LA / LO stand for the latitude / longitude (in decimal degrees) of the sampling site location.

  in terms of Quality for C:N (RDQualC:N) or for C:P (RDQualC:P) was then estimated in each environment by calculating the weighted variance (σ²) of the stoichiometric mismatches in C:N or C:P of all available resources: σ² = ∑ pi . ( SMi(C:N or C:P) -µ* )² (4)

Figure 1 :

 1 Figure 1: Phylogenetic tree of the 15 isopod species, plotted along with the resource availability in their environments and their trophic selectivity PS'. Resource availability in the environment is expressed in grams of Organic Carbon (OC) per square meter.

Figure 2 :

 2 Figure 2: Relative abundance and use of trophic resources in the 15 sampling sites.

Figure 3 :

 3 Figure 3: Relationships between the trophic selectivity PS' and (a) the Resource Availability (RA) as grams of OC . m -2 ; (b) the Resource Diversity in terms of Quantity (RDQuant); (c) and (d) the Resource Diversity in terms of stoichiometric Quality based on C:N and C:P ratios respectively (RDQualC:N and RDQualC:P).

Table 1 :

 1 Description of the 15 isopod species.

	Species

ACKNOWLEDGEMENTS

We thank the following collaborators and organizations for their precious help in collecting biological material: Alhama de Granada municipality, M. Isabel Carreira, T. Datry, D. Eme, Cueva de Valporquero, Junta de Andalucia (Hundidero -Gato) and Rafael Orozco. We are grateful to David Eme for valuable comments on statistical analyses and Pierre Marmonier for a careful reading and editing of the manuscript. We thank the editors and the anonymous reviewers for their valuable comments on an earlier version of this manuscript.

FUNDING

This work was supported by grants from the Agence Nationale de la Recherche (ANR-08-JCJC-0120-01 DEEP) and the Centre National de la Recherche Scientifique (APEGE 2012 'Stoichiogenomics' No. 70632 and EC2CO 2013-2014 'Ecosphere Continentale et Cotiere' -'CoCoNutS' project).

DATA ACCESSIBILITY

Datasets supporting this article are available in the electronic supplementary material. Raw isotopic data are available from the Dryad Digital Repository (https://doi.org/10.5061/dryad.v9s4mw6qw).

AUTHORS' CONTRIBUTIONS

All authors conceived the ideas and designed methodology; CF, LS, TL, FM and CD collected field data; CF, FMB and LS carried out the elemental and stable isotope analyses, analysed the data and wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

COMPETING INTERESTS

We have no competing interests.

SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article.

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Env. Models are ranked by decreasing Akaike's information criterion corrected for small sample size (AICc). A difference of AICc between two models is considered as a significant improvement in favour of the model with the lowest AICc [START_REF] Burnham | Formal inference from more than one model: multimodel inference (MMI). Model Sel. Multimodel inference. A practical information-theoretic approach[END_REF]. PS', RA, RDQual C:N and RDQuant represent the trophic selectivity, the Resource Availability (as g of OC / m²), the Resource Diversity in terms of Quality (based on C:N ratios), and in terms of Quantity, respectively (see main text for details). The 'adjusted R 2 ' (modified R 2 that has been adjusted for the number of predictors in the model) is indicated here as an estimation of the proportion of variance explained by the corresponding model.

Resource


Dr Mary De Feudis
email: mary.defeudis@lspm.cnrs.fr

Alexandre Tallaire
email: alexandre.tallaire@chimie-paristech.fr

Dr Louis Nicolas

Dr Ovidiu Brinza

Dr Philippe Goldner

Gabriel Hétet

Prof Fabien J Bénédic

Dr Jocelyn Achard

Achard

Dr A Tallaire

Large scale fabrication of highly emissive nanodiamonds by chemical vapor deposition with controlled doping by SiV and GeV centers from a solid source

Keywords: color centers, nanodiamond, chemical vapor deposition, quantum technologies, spectroscopy

Fluorescent nanodiamonds (ND) are new promising probes that could outperform organic fluorophores for imaging biological systems. NDs exhibit high brightness without photobleaching and are biocompatible non-toxic materials [1] . The nitrogen-vacancy (NV) defect is one of the most studied color centers in NDs with single photon emission at 637 nm.

In addition, room temperature Optically Detected Magnetic Resonance (ODMR) of the spin levels of the negatively charged NV center has opened interesting perspectives beyond the simple fluorescent contrast imaging in order to sense physical parameters at the nanoscale with high sensitivity such as magnetic or electric fields, temperature or pressure [2] .

Fluorescent NDs have already demonstrated remarkable performance in various areas including single-photon sources in quantum optics [3] , quantitative magnetic field mapping [4] , intra and intercellular molecular processes sensing for biology [5] , cancer cell imaging [6] and effective drug delivery [7] .

In addition to the NV, other color centers are being investigated, in particular those involving group IV elements such as the negatively charged Silicon-Vacancy (SiV), Germanium-Vacancy (GeV) as well as Tin-Vacancy (SnV) defects [8], [9], [10] . Although spin manipulation of group IV defects is not possible at room temperature, they advantageously exhibit emission mostly in their zero phonon line (ZPL) with a much better spectral stability than the NV defect and a much larger Debye-Waller Factor [11] . Single photon emission also occurs at different frequencies (738 nm, 602 nm and 620 nm respectively), which might be of interest for multicolor bio-labelling [12] . Nowadays, two main approaches exist to synthesize NDs at an industrial scale [13] . By detonating explosives in a closed vessel, NDs are produced and then scrapped from the reactor walls. Such detonation NDs need to be separated from other non-diamond particles and cleaned to remove contaminants. Specific post-treatments can then be implemented to improve their luminescence properties [14] . Alternatively, grinding micrometric diamond grits or even monocrystals can allow the production of milled nanodiamonds. In that case, conventional high pressure high temperature (HPHT) diamonds are used as powder to mill. Such NDs can contain relevant amount of nitrogen or metallic impurities, in addition the processing could induces mechanical damage and surface contamination and reduces the ultimate optical properties [15] . By intentionally adding impurities during detonation or conventional HPHT synthesis, specific defects can be produced including group IV color centers [16] . Nevertheless, both these techniques fail to reliably provide highly pure material with a controllable amount of luminescent defects and good optical properties. Timeconsuming post-treatments to remove non-diamond components or to create the desired color centers, such as electron irradiation, are also often required.

More recently, an alternative approach consisting of the direct production of high quality HPHT nanodiamonds by using carbonaceous precursors has been proposed by several research teams with promising results [17], [18], [19], [20] . For example, Bolshedvorski et al. [21] studied nanondiamonds synthetized by a metal-free HPHT method on the basis of a hydrocarbon-containing growth system. In particular, this group succeeded to produce ultrasmall (10 nm) and low-strain diamonds containing an optically active single silicon-vacancy color centers characterized by non-blinking, spectrally narrow emission with narrow distribution of the spectral width. Ekimov et al. [22] as well as Siampour et al. [23] synthetized diamonds from hydrocarbons under high static pressures conditions ensuring a high incorporation of SiV and GeV colour centres. In particular, the synthesis from organic compounds allowed working simply with two parameters (pressure, temperature) and avoid the presence of metallic impurities and inclusions (which are defects typically related to the diamond synthesis starting from the carbon solution based on transition metals). However, limitations in terms of the particles amount produced per bunch as well as the control of SiV and NV undesired traces were registered.

Chemical Vapor Deposition (CVD) has been explored too, and interesting results have been reached for silicon-doped nanodiamonds. In particular, detonation nanodiamonds (a few nm large) are dispersed on a silicon substrate and used as seeds for the diamond growth. By the microwave-plasma exposition, the nanodiamonds grow up to hundreds of nm and incorporate silicon atoms etched away from the substrate. Li et al. [24] characterized such nanodiamonds showing narrow linewidth for the SiV emitters (325 MHz) and high photostability. Zhou et al. [25] demonstrated the coherent control of a strongly driven optical transition of a single SiV defect in a CVD nanodiamond, as well as the long and robust coherence time of SiV emission even at high excitation powers. However as main drawback, this technique allows obtaining just a few NDs per run, which are nucleated and strongly attached to seeded substrates [26] .

Recently we have proposed a new approach that can lead to the mass production of loose NDs with high crystalline quality by CVD [27] . In this work and for the first time, we aim at pushing further the potential of this material by showing that specific group IV color centers can be introduced at a controllable level on demand without any purification or irradiation post-treatment. By the CVD technique we succeeded to produce well-faceted and pure monocrystalline NDs in a quantity of a few mg per run together with a contained amount of smaller polycrystalline particles. We eventually demonstrate that bright NDs containing SiV or GeV emitters with promising optical properties can be engineered which paves the way towards their use in bio-applications or for quantum technologies.

NDs were produced by high microwave power plasma assisted CVD using a H2/CH4 gas mixture (94/6) with intentional addition of a small amount of N2 (0 -5 ppm) or O2 (0 -0.50 %). The powdery deposit that accumulated on the molybdenum holder maintained at a rather low temperature (< 600 °C) was eventually washed out with ethanol to give a dense NDs suspension. The plasma conditions were optimized to lead to a fairly large amount of NDs with good crystalline quality. We estimate NDs production yield to a few mg per run for a duration of 6 h. The CVD technique gives rise to direct particle homogenous nucleation in the gas phase [27], [28] , where thanks the high temperature (3000 K in the plasma core) the thermal dissociation of the chemical species is promoted [29] . Once synthetized the particles move onto the cold holder by thermophoresis. The low substrate temperature together with the seeding absence prevents from the continuous polycrystalline diamond film growth. In addition, the particles deposited on the cold molybdenum substrate do not react ensuring a subsequent very easy and quick removal.

By the CVD technique, the doping was ensured placing a small piece (around 20 × 20 mm²) of crystalline Si or Ge onto the holder, near the region where NDs were created.

Etching of the material by the high-temperature H2-rich plasma led to incorporation of SiV and GeV in the produced diamond nanoparticles. Such doping from a solid source advantageously limits the amount of impurities that are introduced in the chamber as compared to gas phase doping. Besides, high purity materials that are commercially available at low cost and easy to handle can be used unlike toxic gases such as silane or germane (SiH4 or GeH4) [30] that require specific dedicated gas lines. The main drawback though comes from the limited ability to control doping level since the amount of sputtered material from the solid source cannot be easily measured nor varied during growth. In this work, however, we show that the doping level of incorporated impurities can be tuned depending on the growth conditions.

Figure 1 shows the typical morphology of CVD NDs. In Figure 1a the white ND powder dispersed in an ethanol solution is shown. A topview SEM picture of nanoparticles spin-coated on a silicon wafer is reported (Figure 1b). An enlarged SEM picture (Figure 1c) shows clear crystalline facets with some of them sometimes showing twinning. The particles size is in the range 200 -400 nm. A closer look at the TEM images (Figure 1d) also supports the fact that under these optimized growth conditions, NDs are well facetted without any obvious graphitic or amorphous contribution.

The electron diffraction pattern (inset of Figure 1d) where well-defined diffraction spots from the reciprocal lattice are seen, is indeed indicative of monocrystalline particles [START_REF] Zhang | Synthesis of Diamond Using Spark Plasma Sintering[END_REF] . We note twins though that a second group of NDs with a smaller size (30-50 nm) and round-shape were also produced (lower left corner of Figure 1c). These particles were probably nucleated and grown by a different mechanism as confirmed by the TEM characterization (see Supporting Information, Figure S1). These particles exhibit a structure made of aggregated 20 nm large nanocrystals. The corresponding electron diffraction pattern (see Supporting Information,

Figure S1 inset)

shows diffraction rings from the reciprocal lattice, which are indicative of multiple crystalline orientations [START_REF] Zhang | Synthesis of Diamond Using Spark Plasma Sintering[END_REF] .

In this study, in particular for the luminescence assessment, we essentially focused on the larger single crystalline NDs that bear a stronger potential as fluorescent probes. Raman analysis presented in Figure 1e indeed shows a well-defined diamond Raman peak at 1330 cm -1 with a FWHM of about 10 cm -1 , without any obvious graphitic or amorphous contribution. This highlights that our CVD growth approach is able to mass-produce loose NDs with high crystalline quality. However the diamond peak position present a slight shift respect to the conventional position (1332 cm -1 ) which can be related, together with a not negligible full with half maximum, to strain of the CVD nanodiamonds. For completeness, it is useful indicated to presence of an additional band at around 1450 cm -1 , which can be ascribed to vibrations of trans-polyacetylene molecules [START_REF] Zaitsev | Optical Properties of Diamond: A Data Handbook[END_REF] . This band is typically enhanced in the CVD diamond materials deposited at low temperatures.

We then investigated the room temperature luminescence properties of in-situ doped NDs. Cathodoluminescence (CL) analysis of NDs produced with different gas phase compositions is presented in Figure 2. In the presence of a nearby solid silicon source, SiV -color centers emitting at 738 nm were easily incorporated (red curve of Figure 2a). This is consistent with previous reports that have shown that NDs seeded and grown on silicon wafers [START_REF] Nicolas | [END_REF], [34], [35] are contaminated by impurities from the substrate. In this study though, substrate-free and seedless NDs are easily dispersible and produced in a fairly large amount.

N2 additions as low as 5 ppm during growth dramatically enhanced SiV -emission leading to bright NDs (blue curve of Figure 2a). We note that low incorporation of nitrogen was detectable from the signature of NV 0 color centers in CL. NV -centers might be present too but are not efficiently excited in CL. Enhancement of SiV -emission by the presence of N2 is possibly due to the stabilization of this particular negative charge state. Indeed, substitutional nitrogen atoms being deep electron donors in diamond, they may provide free electrons and favor the formation of SiV -centers as already reported both for diamond films and nanoparticles [8], [36] .

Addition of O2 (0.25 %) during growth had the opposite effect, leading to a reduction of SiV -emission (black and green curves of Figure 2a). Indeed, due to the plasma activity, the oxygen molecules reach the dissociation conditions and can react easily with the silicon atoms etched from the wafer. The strong bond strength between oxygensilicon atoms (800 kJ/mol [37] ) make this silicon atom unavailable for incorporation into NDs. By increasing the added O2 amount to 0.5% we confirmed that SiV -emission was further reduced (Figure S2). This study demonstrates that by this growth method it is possible to tune with high flexibility the SiV -emissivity by adding an appropriate amount of N2 or O2 even by using a solid-state doping source.

Growing germanium-doped NDs using only a solid Ge source revealed experimentally more difficult. In particular, the first nanodiamonds were grown using standard plasma conditions together with a germanium solid-wafer and CL investigations showed a very weak emission at 602 nm, characteristic of GeV -centers (black curve of Figure 2b). This emission was not visible in all NDs. In addition, unintentional doping by Si was sometimes detectable, most likely due to contamination from the silica walls of the reactor chamber. Non

Intentionally Doped (NID) SiV -level varied from run to run and depended on the considered nanoparticle.

Addition of a low amount of N2 (5 ppm) led to a drastic increase of GeV -emission which became detectable in all the particles and rather intense (blue curve of Figure 2b).

However, it also enhanced emission from NV 0 and NID SiV -. These latter were in general at about the same level as GeV -. Likewise the silicon-doped NDs, the nitrogen impact on the GeV -emissivity can be ascribed to the stabilization of this particular negative charge state as substitutional nitrogen atoms may provide free electrons and favor the formation of those color centers.

Interestingly, addition of O2 (0.25 %) did not seem to affect emission from GeV - centers but drastically and systematically reduced NID SiV -(red curve of Figure 2b). Since the oxygengermanium binding energy (657 kJ/mol [START_REF] Brewer | Advances in High Temperature Chemistry[END_REF] ) is lower than that for oxygensilicon (800 kJ/mol [37] ), it may explain that the reduction effect was more obvious for Si than for Ge impurities. Nevertheless, theoretical investigations taking into account physicalchemical reactions inside the plasma would be needed to fully describe the doping mechanisms involved. Co-doping by O2 and N2 however revealed efficient to promote emission from GeV -centers at 602 nm while cutting down that from other luminescent impurities (green curve of Figure 2b).

Based on these optimized growth conditions, the production of bright NDs doped with either SiV -or GeV -centers on demand and with a tunable level was demonstrated. Further photoluminescence (PL) characterizations performed by a 473 nm laser at room temperature validated these results (see Figure 2c and Figure 2d). The PL spectra were registered on the brightest silicon-doped (Si solid source and 5 ppm N2) and germanium-doped (Ge solid source, 5 ppm N2 and 0.25 % O2) NDs. The prominent brightness of both color centers is here easily appreciable using the diamond Raman peak (labelled "d") as a reference. It is worth saying that no post-treatment (thermal annealing, chemical cleaning or irradiation) was necessary to obtain such a bright and high quality nanodiamonds. Subsequently, the optical properties of CVD NDs were evaluated at low temperature.

The optical properties of the NDs at cryogenic temperatures are presented in Figure 3. Figure 3a shows a confocal scan of nanodiamonds containing SiV centers that are cast on a quartz cover-slip. The presence of single diamond nanoparticles and nanodiamond clusters is manifest from the change in the SiV photoluminescence from one area to another.

Figure 3b shows a photoluminescence spectrum obtained at 10 K. Several narrow peaks can be observed from 735 nm to 742 nm. Such a spectral dispersion is due to strain inhomogeneity [START_REF] Meesala | [END_REF] . Given that each peak is well isolated from the other, they most likely all belong to a single emitter. The true emitter linewidth cannot be measured directly with our spectrometer as it appears to be lower than its resolution. To go beyond, Photoluminescence Excitation (PLE) on the SiV -line marked with a red arrow, using 100 nW of resonant

(a) (b) (c) (d)
excitation scanned around the line was performed. PL in the phonon sidebands is measured as the laser frequency is scanned and the spectrum is plotted in Figure 3b. The measured linewidth on this line is 911 MHz, which is about a factor of 3 to 4 times the natural linewidth.

This result is very promising as it means that spectral diffusion is low, implying that the symmetry of the SiV -centers is likely to be preserved [40] . Nevertheless, to confirm that, systematic analyses by a faster PLE read form single SiV centres would be required.

Similar PL spectra have been measured at low temperature using nanodiamonds containing GeV -centers. One of them is shown in Figure 3c. Several spectrally resolved lines are also visible. Another spectrum is shown in Figure 3d and a good photostability has been measured by monitoring the PL over time. One of the two peaks is very stable both from the point of view of PL rate and spectral diffusion. Unfortunately, PLE on GeV -centers could not be performed because we lacked a tunable laser at 605 nm. Nevertheless these measured optical properties for both SiV and GeV color centers are extremely promising within the context of quantum technologies that require that a controllable density of luminescent defects with narrow linewidth are incorporated into nanoscale diamonds.

In this work a new approach for high-quality nanodiamond synthesis is proposed. The CVD technique optimization allowed producing well-faceted and pure monocrystalline NDs in a large quantity (a few mg per run) without using any seeded substrate. A small amount of round polycrystalline (30 -50 large) particles can be also collected after the deposition steps.

Importantly, these NDs can be easily dispersed in solution which facilitates their further use or integration in a sensor, or a photonic cavity [41], [42] . With the aim of pushing further the potential of this method, the incorporation of specific group IV color centers has been demonstrated at a controllable level on demand without any purification or irradiation posttreatment. In addition, a high control of the SiV -and GeV -emissivity has been reached and tuned by playing with the gas phase composition of the plasma environment. The optical characterization showed narrow optical lines at cryogenic temperatures, very close to the lifetime limit, and high photostability for both the SiV and GeV centers. Therefore, it is believed that such bright NDs containing SiV or GeV emitters can be engineered so as to open the way to their use in bio-applications or for quantum technologies.

Experimental Section

Nanodiamond growth

Diamond nanoparticles were grown by the Microwave Plasma Assisted Chemical Vapour Deposition (MPACVD) technique. The reactor was a BJS150 model provided by Plassys company. The nanodiamond powder was grown with a gas mixture of H2/CH4 (94/6) under fixed power and pressure conditions (2.7 kW / 155 mbar). After six hours of deposition, nanodiamonds were collected from a molybdenum substrate holder and dispersed in an ethanol solution.

SEM and TEM observation:

SEM characterizations were carried out by Zeiss Supra VP 40 system. The nanodiamonds were spin coated onto a silicon wafer and analysed under a field effect gun system.

TEM images were registered by a JEOL EM2010 system. The NDs suspension drop was casted onto a carbon TEM grid and analysed by a 200 kV microscope.

Raman/PL

Raman and photoluminescence investigations were performed by a Jobin-Yvon HR800 spectrometer using the 473 nm excitation line at room temperature. The light was focused using a long range 100 X objective. A 1800 l/mm grating was used to disperse light onto a Pelletier cooled CCD camera.

Cathodoluminescence:

CL analysis was carried out using a Horiba HCLUE system connected to a ZEISS EVO MA15 equipped with a high brightness LaB6 filament. The electron beam used to create the excitation was set at 10 kV and 20 nA. The light was collected by a parabolic mirror and sent out to a spectrometer equipped with a 600 l/mm grating and a Pelletier-cooled CCD camera.

The slit width was set to 100 µm.

Spectroscopic analysis

The photoluminescence studies shown in Fig. 3 were realized using a homebuilt confocal microscope at cryogenic temperature with an objective that has a numerical aperture of 0.7 and a closed-cycle cryostat (MyCryoFirm). The sample was attached to a cold finger inside a vacuum chamber and cooled down to 10 K. The emitters were off resonantly excited using 4 mW of laser light at 532 nm laser. The photoluminescence was coupled to a monomode fiber and sent to an avalanche photodiode (SPCL-AQRH-15 Excelitas Technologies) or to an optical spectrometer equipped a grating with 1800 l/mm grating and detected by an Andor camera (DU401A-BVF). SiV-resonant excitation was performed using a narrow line tunable diode laser (Toptica DL Pro). The PL signal was collected on the phonon sidebands and filtered using a long-pass filter (Thorlabs FELH0750) to remove the residual laser signal.

Supporting Information

Supporting Information is available from the Wiley Online Library or from the author.

The table of contents entry should be 50-60 words long and should be written in the present tense and impersonal style (i.e., avoid we). The text should be different from the abstract text. SiV -

Figure 1 .

 1 Figure 1. (a) A container with NDs dispersed in ethanol. (b) A representative SEM image of CVD NDs spin-coated on a Si wafer from the suspension. (c) Enlargement of the SEM image (b) showing clear crystalline facets with some NDs having twins. (d) TEM image from a large single crystalline particle and the corresponding electron diffraction pattern (inset). Smaller polycrystalline nanoparticles are also visible. (e) Typical Raman spectrum obtained with a 473 nm laser from which the diamond Raman peak labelled by the letter "d" is visible together with a broader band attributed to transpolyacetylene.

Figure 2 .

 2 Figure 2. Cathodoluminescence of CVD NDs grown with (a) intentional Si doping, (b)intentional Ge doping. N2 (5 ppm) and O2 (0.25 %) were added to the gas phase during growth as indicated in the legend. NID stands for non-intentionally doped. Spectra were vertically shifted for clarity. Photoluminescence excited with a 473 nm laser of highly bright NDs containing SiV -centers (c) using 5 ppm N2 additions, and GeV -centers (d) by co-doping with N2 (5 ppm) and O2 (0.25 %).

Figure 3 .

 3 Figure 3. (a) Confocal raster scan obtained by scanning the sample in front of the microscope objective. The bright spots show the presence of SiV-centers. (b) SiV-PL spectrum under green laser excitation. Inset: PLE spectrum of the SiV-transition marked by a red arrow. The SiV-center is resonantly excited with 104 nW of laser power. (c) GeV-PL spectrum of one nanodiamond (ND1) doped by Germanium under green off resonant excitation. (d) Photoluminescence of a germanium doped nanodiamond (ND2) as a function of time.

  ToC figure ((Please choose one size: 55 mm broad × 50 mm high or 110 mm broad × 20 mm high. Please do not use any other dimensions)) CVD plasma technique allows fabricating large scale nanodiamonds. Particles can be easily collected and dispersed in a liquid solution, without needing any treatment. The high technique flexibility makes it possible to dope nanodiamonds by the different IV group elements, such as silicon, in order to obtain incorporated colour centres. As a result SiV centre high emissive can be registered. Copyright WILEY-VCH Verlag GmbH & Co. KGaA, 69469 Weinheim, Germany, 2018. CVD plasma synthesis Liquid dispersed nanodiamonds Nanodiamond photoluminescence SiV color centers inside a nanodiamond Si wafer Small round nanodiamonds (30 -50 nm) can be obtained by CVD technique. SEM investigations show an aggregated nanocrystal structure with multiple orientations, which is confirmed by the diffraction rings from the reciprocal lattice of the electron diffraction pattern.

Figure S2 .

 S2 Figure S2.Cathodoluminescence of silicon-doped CVD NDs grown without oxygen (blue curve) and with oxygen at the 0.25% and 0.50% (green and red curve, respectively).

Acknowledgements

We acknowledge the financial support from the Region Ile-de-France in the frame work of DIM SIRTEQ, the European Union's research and innovation program under grant agreement No 820394 (ASTERIQS), and by the QUANTERA project n°ANR-18-QUAN-0008-02 MICROSENS.

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Supporting Information