Jean Legeay

Claude Husson

Benjamin Boudier

Eliane Louisanna

Christopher Baraloto

Heidy Schimann

Benoît Marcais
email: benoit.marcais@inra.fr

Marc Buée
email: marc.buee@inra.fr

Surprising low diversity of the plant pathogen Phytophthora in Amazonian forests

Keywords: Biodiversity, Neotropical forests, Janzen-Connell, host specialization

The genus Phytophthora represents a group of plant pathogens with broad global distribution. The majority of them causes collar and root-rot of diverse plant species. Little is known about Phytophthora communities in forest ecosystems, especially in Neotropical forests where natural enemies could maintain the huge plant diversity via negative density dependence. We characterized the diversity of soil-borne Phytophthora communities in the North French Guiana rainforest and investigated how they are structured by host identity and environmental factors. In this little-explored habitat, 250 soil cores were sampled from ten plots hosting ten different plant families across three forest environments (Terra Firme, Seasonally Flooded and White Sand). Phytophthora diversity was studied using baiting approach and metabarcoding (High Throughput Sequencing) on environmental DNA extracted from both soil samples and baiting-leaves. These three approaches revealed very similar communities, characterized by an unexpected low diversity of Phytophthora species, with the dominance of two cryptic species close to Phytophthora heveae. As expected, Phytophthora community composition of French Guiana rainforest was significantly impacted by the host plant family and environment. However, these plant pathogen communities are very small and are dominated by generalist species, questioning their potential roles as drivers of plant diversity in these Amazonian forests.

Introduction

Neotropical forests are major diversity hotspots for plants [START_REF] Jablonski | Out of the Tropics: Evolutionary Dynamics of the Latitudinal Diversity Gradient[END_REF][START_REF] Antonelli | Why are there so many plant species in the Neotropics?[END_REF]Ter Steege et al., 2013). Amongst the multiple hypotheses proposed to explain how plant species coexistence is promoted in these especially diverse forests [START_REF] Wright | Plant diversity in tropical forests: a review of mechanisms of species coexistence[END_REF], environmental filtering and negative density dependence of hostspecific natural enemies have been highlighted [START_REF] John | Soil nutrients influence spatial distributions of tropical tree species[END_REF][START_REF] Mangan | Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest[END_REF][START_REF] Philipson | Light-based Regeneration Niches: Evidence from 21 Dipterocarp Species using Size-specific RGRs[END_REF][START_REF] Bever | Maintenance of Plant Species Diversity by Pathogens[END_REF]. Indeed, the role of pathogens in the maintenance and structure of plant diversity has been a major topic in community ecology [START_REF] Alexander | Disease in Natural Plant Populations, Communities, and Ecosystems: Insights into Ecological and Evolutionary Processes[END_REF][START_REF] Keesing | Impacts of biodiversity on the emergence and transmission of infectious diseases[END_REF], especially in Neotropical forests [START_REF] Mangan | Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest[END_REF][START_REF] Terborgh | Enemies Maintain Hyperdiverse Tropical Forests[END_REF][START_REF] Bagchi | Pathogens and insect herbivores drive rainforest plant diversity and composition[END_REF]. The Janzen-Connell hypothesis could explain the maintenance of plant diversity by an accumulation of host-specific natural enemies near reproductive adults that affects their offspring in the vicinity, thus limiting host plant species abundance and aggregation [START_REF] Janzen | Herbivores and the Number of Tree Species in Tropical Forests[END_REF][START_REF] Schupp | The Janzen-Connell Model for Tropical Tree Diversity: Population Implications and the Importance of Spatial Scale[END_REF][START_REF] Mills | Maintenance of diversity within plant communities: soil pathogens as agents of negative feedback[END_REF]. This hypothesis requires that natural enemies have a significant degree of host specialization, even if they do not have to be strict species-level specialists [START_REF] Bagchi | Pathogens and insect herbivores drive rainforest plant diversity and composition[END_REF][START_REF] Bever | Maintenance of Plant Species Diversity by Pathogens[END_REF]. Alternatively, pathogens may be generalists and structured more by environmental filtering and biogeographic history than by host plant community structure [START_REF] Bever | Maintenance of Plant Species Diversity by Pathogens[END_REF].

Because the genus Phytophthora contains important plant pathogen species, often Phytophthora species are often involved in seedling mortality, with a worldwide presence and an important economic impact [START_REF] Hansen | Phytophthora Species Emerging as Pathogens of Forest Trees[END_REF]. These pathogens could represent a good target to study Janzen-Connell effects and host specialization in Neotropical forests. Indeed, Phytophthora species exhibit a large diversity of aggressiveness and host range [START_REF] Erwin | Phytophthora Diseases Worldwide[END_REF]. By 2018, the genus encompassed 173 described species and eight hybrids, with an estimation of 326 total species (Scott et al., 2018), divided in twelve phylogenetic clades, corresponding to aquatic opportunists, foliar pathogens, or soil-borne pathogens [START_REF] Hansen | Phytophthora Species Emerging as Pathogens of Forest Trees[END_REF]. However, Phytophthora communities have been understudied in Neotropical forests.

The few studies on Phytophthora in tropical forests have focused on characterizing Phytophthora diversity, without explicit focus on the factors shaping these communities. For example, Zeng et al., 2009, found 14 species in the tropical island of Hainan, sampled from plant materials, soils or water at five locations, although only five species were found in forest. In their study across Australia, Burgess et al., 2019, sampled 20 locations in the subtropical/tropical Queensland and found 13 Phytophthora species, among which the most frequent were P. cinnamomi and P. heveae. [START_REF] Wallace | Diversity of Phytophthora Species in Costa Rica's Tropical Forest[END_REF], showed that Phytophthora diversity could be relatively high in Neotropical forests of Costa Rica, with 21 species found in various forest locations. Some species such as P. cinnamomi, P. heveae, P. castaneae were common across these studies and appear to be widespread in tropical forests [START_REF] Zeng | A Survey of Phytophthora Species on Hainan Island of South China[END_REF][START_REF] Wallace | Diversity of Phytophthora Species in Costa Rica's Tropical Forest[END_REF][START_REF] Burgess | Distribution and diversity of Phytophthora across Australia[END_REF][START_REF] Jung | Diversity of Phytophthora species in natural ecosystems of Taiwan and association with disease symptoms[END_REF].

A traditional method for the study of soil-borne Phytophthora communities is the baiting technique. This approach involves use of plant tissues, usually leaves, as baits on which the targeted pathogens produce characteristic lesions. Isolation onto selective agar medium from the lesions enables detection and determination of species using molecular barcodes. More recently, high-throughput sequencing (HTS) methods were applied to study Phytophthora communities [START_REF] Vettraino | Pyrosequencing as a tool for the detection of Phytophthora species: error rate and risk of false Molecular Operational Taxonomic Units[END_REF][START_REF] Coince | Below-ground fine-scale distribution and soil versus fine root detection of fungal and soil oomycete communities in a French beech forest[END_REF][START_REF] Català | The Use of Genus-Specific Amplicon Pyrosequencing to Assess Phytophthora Species Diversity Using eDNA from Soil and Water in Northern Spain[END_REF][START_REF] Burgess | Distribution and diversity of Phytophthora across Australia[END_REF], Català et al., 2017;[START_REF] Bose | Community composition and distribution of Phytophthora species across adjacent native and non-native forests of South Africa[END_REF]. With this approach, DNA is directly extracted from environmental samples (e.g. soil), then DNA barcodes are amplified with Phytophthora-specific primers and sequenced for analysis [START_REF] Scibetta | A molecular method to assess Phytophthora diversity in environmental samples[END_REF]. These methods are less timeconsuming and show greater sensitivity [START_REF] Shokralla | Next-generation sequencing technologies for environmental DNA research[END_REF], potentially circumventing biases such as seasonal variation of Phytophthora biomass or activity, and possible specificity of the leaves used as bait toward some Phytophthora species [START_REF] Tsao | Selective Isolation of Species of Phytophthora from Natural Soils on an Improved Antibiotic Medium[END_REF]. Several studies have compared these two approaches, [START_REF] Vannini | Pyrosequencing of environmental soil samples reveals biodiversity of the Phytophthora resident community in chestnut forests[END_REF][START_REF] Català | Development of DNA massive sequencing techniques and RealTime PCR for the detection, identification and quantitation of Phytophthora spp[END_REF][START_REF] Bose | Community composition and distribution of Phytophthora species across adjacent native and non-native forests of South Africa[END_REF][START_REF] Khaliq | eDNA from roots: a robust tool for determining Phytophthora communities in natural ecosystems[END_REF]. The HTS approach generally enables detection of a much higher number of Phytophthora taxa. For example, in [START_REF] Bose | Community composition and distribution of Phytophthora species across adjacent native and non-native forests of South Africa[END_REF], five taxa were detected by baiting whereas 32 taxa were detected by metabarcoding.

In this study, we sampled soil at the stem collar of individuals from tree species belonging to ten different taxonomic families in each of three soil conditions at two different locations in French Guiana. We used baiting/isolation and metabarcoding methods to describe Phytophthora communities in these different environments. Our aims were (1) to compare the methods (2) to estimate the diversity of these communities and compare it to diversity of other tropical and temperate studies, and (3) to unravel the primary factors that structure Phytophthora communities in the soils of these Neotropical forest sites. We hypothesized (i) that soil metabarcoding will provide greater precision in community estimates than traditional baiting techniques, (ii) that Phytophthora richness will be similar to other sampled moist tropical forests, higher than that found in temperate forests, and (iii) that host plant identity will play a stronger role than environmental filtering in structuring Phytophthora communities.

Results

Phytophthora diversity uncovered by baiting & isolation

Out of the 93 soil samples, 89 produced lesion spots on Rhododendron leaves, 92 on Avocado leaves and 91 on Cassava leaves. From these 272 bait leaves, 581 isolates were obtained from infection spots, out of which 366 were identified as Phytophthora species (Fig. 2). Among these Phytophthora isolates, 136 were found in TF soils, 128 in SF soils and 102 in WS soils. Phytophthora retrieval success depended on the bait and location (both p<0.001), but not on the environment (P= 0.83) or host family (P= 0.42). Fifty out of the 55 samples (91%) from the CSG site produced isolates that were identified as Phytophthora through Sanger sequencing, while only 24 out of the 38 samples (63 %) from the Laussat site produced isolates. Phytophthora strain recovery was high on Rhododendron leaves (167 isolates) and Avocado leaves (127 isolates), but lower on Cassava leaves (72 isolates).

Figure 2. Experiment design with the number of samples at each step, from the sampling to the metabarcoding or isolation. Among 100 potential "tree families x plots" combinations, 93 individuals were sampled because some plant families could not be found in all plots.

The number of lesions per leaf weight was quantified on Rhododendron bait leaves as a measure of the presence of live Phytophthora in the baited soils. The number of lesions per leaf weight was not related to the location nor to the environment (Fig. 3). However, the number of spots was significantly different between host plant families with a strong "family x environment" interaction (both p<0.001). Figure 3 shows that while many families had higher lesion number on TF environments compared to WS or SF, other families (Malvaceae, Moreceae, Myrtaceae) showed limited differences between the environments. Sanger sequences of the ITS region of the Phytophthora isolates were approximately 800 bp in length (min 616 bp -max 876 bp), versus from 373 to 382 bp for RAS-YPT gene. Sequencing of the ITS region enabled us to identify five potential species: a species close to P. heveae (named here P. heveae-like, 304 isolates), a species close to P. castaneae (named here P. castaneae-like, 24 isolates), P. cinnamomi (23 isolates), P. sp. cuyabensis (6 isolates) and an undescribed species from clade 9, P. sp. neb_1543 (9 isolates). Isolates identified as P. heveaelike, P. castaneae-like and P. sp. cuyabensis showed some heterogeneity of ITS sequences, differing for up to 9 bases between P. castaneae-like isolates, up to 4 bases between P. heveae-like isolates and 2 bases between P. sp. cuyabensis isolates. ITS sequence alignments within either P. cinnamomi isolates or P. sp. neb_1543 isolates showed 100% homology. Clustering patterns and assignation from RAS-Ypt and ITS sequences were similar, although variability between RAS-Ypt sequences was lower (up to 3 bases between P. heveae-like isolates, up to 2 bases between P. castaneae-like isolates, and no variability among isolates of other species). Three plant species used as bait leaves enabled us to retrieve very similar Phytophthora communities, with one exception -P. sp. cuyabensis, the least frequent species, was not recovered from Cassava leaves. Six P. cinnamomi isolates that were selected randomly among the 23 retrieved isolates, produced oospores after crossing with P. cinnamomi mating type A1, and none after crossing with mating type A2, suggesting that their mating type corresponded to A2.

Figure 4: Bayesian inference phylogenetic trees of 14 P. castaneae-like and P. heveae-like isolates created by concatenation of nuclear genetic markers (ITS, RAS-Ypt, Enolase). Reference isolates taken from NCBI are designated by their accession numbers and origins in Table S1. Isolates from this study are designated by either P. heveae-like or P. castaneae-like. Monophyletic groups of isolates were clustered as groups which were used for further analysis. Distances are given in number of substitutions/site.

Phylogenetic analysis of isolates

Figure 4 shows the phylogenetic tree built from the three nuclear markers (ITS, RAS-Ypt, Enolase), either sequenced from the studied set of clade-5 isolates or retrieved from GenBank (Table S1). The isolates retrieved from our study clustered into four separate groups. Two of them were close to P. heveae, and the two others were phylogenetically close to P. castaneae. Bayesian inference of these Phytophthora strains from nuclear data revealed that distance between respectively the two P. heveae-like groups and the two P. castaneae-like groups from French Guiana were equal or greater than between reference isolates from different species of clade-5: P. castaneae, P. cocois, P. agathidicida and P. heveae (Fig. 4). Thus, these clade-5 isolates of Phytophthora could potentially belong to new species and were treated as such for the rest of this study. These four Phytophthora groups from clade-5 could be separated based on the ITS sequence alone, although the Enolase sequence was more discriminant. This enabled us to attribute each of the 304 P. heveae-like and 24 P. castaneaelike isolates retrieved from the baiting/isolation method to one of the 4 groups. On the other hand, for mitochondrial markers, all 15 isolates from clade-5 clustered with the reference P. heveae (Fig. S1); the mitochondrial phylogeny of the clade-5 isolates was thus not congruent with their nuclear phylogeny. While the two P. heveae-like groups could still be separated based on their mitochondrial markers, the two P. castaneae-like groups were merged with one of the P. heveae-like group.

The four P. cinnamomi isolates studied did not exhibit polymorphism for any of the studied markers.

Table 1: List of main Phytophthora species identified from environmental samples with the three methods, with the proportion (%) of samples containing each species.

Metabarcoding approach and comparison with baiting/isolation

The mean read number per sample was 618 ± 244 for direct metabarcoding from soil samples, and 805± 146 for leaf samples. The samples covering less than 72 reads were discarded. Indeed, a minimum depth of 72 reads was the compromise that allowed keeping most soil samples, while still describing all species of Phytophthora retrieved in the communities. Consequently, 52 soil sample libraries and 223 bait leaves libraries were used in the analysis. Both sets of data gave a mean proportion of 1% non-Phytophthora reads. Phytophthora reads from these samples were clustered in OTUs with a 99% similarity threshold, corresponding to five Phytophthora species only (Table 1). The R2 read length was around 400 nucleotides long in the metabarcoding libraries, while the Sanger ITS sequences previously discussed were c. 800 nucleotides long. As a consequence, ITS polymorphism of these shorter ITS fragments did not allow separation of the two P. castaneae-like groups, nor the two P. heveae-like groups, as could be done with the baiting/isolation results. Among the species retrieved, the P. heveae complex (P. heveae and/or the P. heveae-like groups) was present in every single sample and represented an average of 97% of the Phytophthora reads. Other detected species were P. cinnamomi, the P. castaneae complex, Phytophtora. sp. neb_1543 and a new putative species from clade 10, Phytophthora sp. nov 10.

The species communities obtained with the metabarcoding method differed from the one retrieved with baiting/isolation methods by one species only found by metabarcoding (Phytophthora sp. nov 10) and by one species only found by baiting/isolation (P. sp. cuyabensis). As a result, there was a significant difference in the qualitative composition of communities retrieved with each of the three methods previously described (p=0.001), with the baiting / isolation method showing a lower frequency of the P. heveae complex and an increase in the rare species frequency (Table 1). There was no significant difference in the recovered Phytophthora communities between the bait leaf species (Rhododendron, Cassava, Avocado) (p=0.9). A: A significant difference was detected between families (p=0.004) but not between higher clades (p= 0.166). B: There was a significant difference between the two locations Laussat and CSG (p= 0.001) and between environment types (p= 0.017). TF=Terra firma. SF=seasonally flooded. WS = white sand.

Community structure analysis

The average number of species per plot was 1.4, with a dominance of the P. heveae complex as mentioned above. Taking into account the four new putative clade-5 species in the analysis of the Phytophthora communities described by the baiting and isolation method, we showed a strong location (CSG vs Laussat) effect (p=0.001). Furthermore, a significant effect of forest environment (TF, SF and WS) (p=0.017) was also detected (Fig. 5). The Shannon and Simpson diversity indices were higher in CSG than in Laussat and were higher in SF and TF environments compared to WS (Fig. 5) .A significant effect of host plant family was detected (p=0.004), with an interaction between the family and the location (p=0.029). These results were congruent with analyses of Bray-Curtis similarity indices computed between communities (Table S2). Indeed, these similarity indices computed between the two locations (CSG vs Laussat) were low (0.31), while they were above 0.83 between environments. In addition, the Shannon and Simpson indices showed important variation between plant families (Fig. 5). However, at higher clade levels (Fig. 5A), no effect was detected between Asterids, Rosids and Magnoliids (p=0.166).

To compare baiting/isolation results with metabarcoding results, we had to merge the two P. heveae-like species as well as the two P. castaneae-like species, as they could not be distinguished in the metabarcoding data. Combining the two approaches, no environment type effect was detected (p=0.165). An effect of the host trees taxonomy was still detected at the family level (p=0.017), with an interaction with the method used (p=0.014) (Fig. 6). A significant location effect between CSG and Laussat was detected with all methods (p=0.001), with a strong interaction with the method used (p=0. 002). Figure 6. Proportion of samples containing the observed species of Phytophthora, grouped by environment type (A) and location (B). A: Significant difference between the different environment types was detected (p=0.003), with seasonally flooded soils containing more rare species. B: Significant difference between the two locations was detected (p=0.001).

Discussion

Although the observed Phytophthora community structure depended on the approaches used, our results validated the benefits of HTS for large-scale surveys. Both HTS and baiting approaches uncovered a surprisingly low diversity of Phytophthora in the soils of these two French Guiana locations, with only six species retrieved and a strong dominance of the P. heveae complex in all samples. Moreover, no host specialist Phytophthora species were identified. Despite this low diversity, the communities were to some degree structured by the plant hosts with a significant family effect, suggesting a certain degree of host preference.

Comparison between baiting/isolation and metabarcoding

Baiting and molecular approaches have been described as [START_REF] Català | The Use of Genus-Specific Amplicon Pyrosequencing to Assess Phytophthora Species Diversity Using eDNA from Soil and Water in Northern Spain[END_REF]. Baiting techniques are time-consuming, and they may not detect low inoculum. Conversely, metabarcoding methods allow fast and massive pathogen detection. In our study, similar patterns were uncovered by the three approaches. Indeed, the three most abundant species were retrieved with all methods, and taxa of the P. heveae complex were largely dominant. However, the use of bait leaves followed by isolation increased the proportion of rare species compared to simple metabarcoding from soil or bait leaves. The isolate morphological observations in the initial step of the baiting/isolation method probably biased the selection toward rare species. Indeed, baiting could be advantageous to investigate communities from larger soil samples (200 ml versus about 0.5 ml). However, it did not enable us to detect additional diversity; rather, it proved to be a time-consuming additional step with little use in the metabarcoding approach. Our results therefore contrast with previous work in which metabarcoding methods were more efficient to describe Phytophthora community structure [START_REF] Vannini | Pyrosequencing of environmental soil samples reveals biodiversity of the Phytophthora resident community in chestnut forests[END_REF][START_REF] Català | Development of DNA massive sequencing techniques and RealTime PCR for the detection, identification and quantitation of Phytophthora spp[END_REF][START_REF] Bose | Community composition and distribution of Phytophthora species across adjacent native and non-native forests of South Africa[END_REF][START_REF] Khaliq | eDNA from roots: a robust tool for determining Phytophthora communities in natural ecosystems[END_REF]. The suitability of the baiting/isolation method appears to depend largely on the Phytophthora community sampled, on sampling conditions, and on environmental factors. Indeed, baiting can be dependent on time of sampling, quantity and type of inoculum in the soil, species and size of the bait leaves, as well as soil parameters and origins. Timing of the sampling might for example explain the difference in Phytophthora recovery between the two sites we studied, which were sampled almost two months apart. One shortcoming of the metabarcoding method in this study is its inability to discriminate between the two putative P. heveae-like species in addition to the two putative P. castaneae-like species, because of the shorter length of the ITS fragment used. The baiting method does enable the isolation of living cultures for further characterisation; nevertheless, the less time-consuming metabarcoding from soil DNA was sufficient to obtain the bulk of the results in our study.

Diversity of French Guiana communities of Phytophthora

The number of putative Phytophthora species observed in these French Guiana soils is lower than what is found in other forest ecosystems, even though our study sampled broadly across plant species from 10 different families representing the breadth of Angiosperm diversity, and across three very different environments. This result is surprising in one of the major biodiversity hotspots of the world and given the tendency of other organisms, including every studied fungal guild, to increase in diversity at lower latitudes [START_REF] Tedersoo | Global diversity and geography of soil fungi[END_REF][START_REF] Willig | Latitudinal Gradients of Biodiversity: Pattern, Process, Scale, and Synthesis[END_REF]. Surveys in temperate, Mediterranean and tropical forests of several world regions have revealed a diversity of Phytophthora of 4-10 species per sampling location [START_REF] Jung | Involvement of soilborne Phytophthora species in Central European oak decline and the effect of site factors on the disease[END_REF][START_REF] Balci | Phytophthora spp. Associated with Forest Soils in Eastern and North-Central U.S. Oak Ecosystems[END_REF][START_REF] Oh | Surveys of soil and water reveal a goldmine of Phytophthora diversity in South African natural ecosystems[END_REF][START_REF] Scanu | Diversity of Phytophthora Species from Declining Mediterranean Maquis Vegetation, including Two New Species, Phytophthora crassamura and P. ornamentata sp. nov[END_REF][START_REF] Sims | The Phytophthora species assemblage and diversity in riparian alder ecosystems of western Oregon, USA[END_REF][START_REF] Burgess | Distribution and diversity of Phytophthora across Australia[END_REF][START_REF] Català | Metabarcoding and development of new real-time specific assays reveal Phytophthora species diversity in holm oak forests in eastern Spain[END_REF][START_REF] Bose | Community composition and distribution of Phytophthora species across adjacent native and non-native forests of South Africa[END_REF][START_REF] Redondo | Functional traits associated with the establishment of introduced Phytophthora spp. in Swedish forests[END_REF][START_REF] Mora-Sala | Diversity of Phytophthora Species Associated with Quercus ilex L. in Three Spanish Regions Evaluated by NGS[END_REF]. As an example, [START_REF] Burgess | Distribution and diversity of Phytophthora across Australia[END_REF], found a mean of 5 species per sampling location in a wide survey across Australia.

The low Phytophthora diversity we observed in French Guiana could be explained by several factors. First, we investigated two sites both located in the Guyana costal range under similar climatic conditions of moist seasonal forests. Wallace, 2015, found a wider diversity of Phytophthora in Neotropical forests by investigating locations of differing hygrometry and altitude (plain rainforests vs. mountain cloud forests). In the same study, the author suggests that a greater diversity can be found in water streams. Also, in our study, we investigated soil adjacent to non-symptomatic juvenile trees irrespective of their proximity to conspecific adult trees, and host-specific Phytophthora species might not have accumulated on them if they were not adjacent to conspecific adults. Finally, even coastal forests in French Guiana receive much less exposure to human disturbance than many other tropical and temperate forests. Many common Phytophthora species have been shown to be invasive and spread through worldwide trade [START_REF] Santini | Biogeographical patterns and determinants of invasion by forest pathogens in Europe[END_REF][START_REF] Jung | Widespread Phytophthora infestations in European nurseries put forest, semi-natural and horticultural ecosystems at high risk of Phytophthora diseases[END_REF], and human occupancy and history of trading could be decisive factors for predicting Phytophthora diversity. Indeed, Phytophthora diversity has been linked to the degree of anthropogenic disturbance [START_REF] Dale | Using genomic data to understand anthropogenic influences on Oomycete and Phytophthora communities, and the evolution of an alien invasive species responsible for sudden oak death, Phytophthora ramorum[END_REF][START_REF] Redondo | Functional traits associated with the establishment of introduced Phytophthora spp. in Swedish forests[END_REF], with the average number of Phytophthora species by location reaching nine in urban environments and only six in natural environments [START_REF] Dale | Using genomic data to understand anthropogenic influences on Oomycete and Phytophthora communities, and the evolution of an alien invasive species responsible for sudden oak death, Phytophthora ramorum[END_REF]. In addition, Ruiz Gomez et al. ( 2019) find alpha-diversity of soil pathogenic Oomycetes was not correlated to the alphadiversity of plants in Andalusian forests, but it would seem to be more related to the nature of the fungal community present in the soil. This calls for more standardized community descriptions in various parts of the world, especially tropical areas, which would allow future meta-analyses of Phytophthora diversity and composition, perhaps in link with other soil microorganisms communities such as fungi.

Strikingly, the three dominant Phytophthora groups we observed in French Guiana, P. heveae, P. castaneae and P. cinnamomi are also the dominant Phytophthora groups in Hainan and Queensland soils [START_REF] Zeng | A Survey of Phytophthora Species on Hainan Island of South China[END_REF][START_REF] Burgess | Distribution and diversity of Phytophthora across Australia[END_REF]. This suggests a similarity of the Phytophthora communities in tropical soils from South Asia to Australia and South America. For P. heveae and P. castaneae, this similarity could be spurious, as we found that they are likely closely related but different species. The low, but recurrent, nucleotide polymorphism of ITS sequences obtained from genotyped strains further suggests a potential species complex. The analysis of additional markers confirms that both putative species are heterogeneous and probably distinct from P. heveae and P. castaneae. The existence of isolates close but distinct from P. heveae and P. castaneae have already been reported by [START_REF] Zeng | A Survey of Phytophthora Species on Hainan Island of South China[END_REF] Hainan. Clade 5 has therefore recently been revised [START_REF] Weir | A taxonomic revision of Phytophthora Clade 5 including two new species, Phytophthora agathidicida and P. cocois[END_REF]. However, part of the taxon of this P. hevea and P. castaneae species complex could be hybrid. Indeed, hybridization has been shown to be a common feature in natural populations of Phytophthora [START_REF] Burgess | Molecular Characterization of Natural Hybrids formed between five Related Indigenous Clade 6 Phytophthora Species. Édité par Mark Gijzen[END_REF]. Thus, the discrepancy between the nuclear and mitochondrial markers of the P. heveae and P. castaneae complexes could be an indicator of hybridization events. In addition, the ambiguity on topology of both phylogenetic trees could possibly be due to an introgressive hybridization of a P. heveae-like 2 mitochondrial genome in the two P. castaneae-like groups, as mitochondrial genes are inherited as a block during these events [START_REF] Rubinoff | Between Two Extremes: Mitochondrial DNA is neither the Panacea nor the Nemesis of Phylogenetic and Taxonomic Inference[END_REF][START_REF] Bachtrog | Extensive introgression of mitochondrial DNA relatives to nuclear genes in the Drosophila Yakuba species group[END_REF].

P. cinnamomi is considered invasive in more than 70 countries [START_REF] Erwin | Phytophthora Diseases Worldwide[END_REF], and the six isolates we tested showed 100% homology in the 6 tested loci. Furthermore, these six randomly selected isolates all belong to only one mating type, which does not suggest a native P. cinnamomi population [START_REF] Erwin | Phytophthora Diseases Worldwide[END_REF]. P. cinnamomi might therefore be an invasive species in French Guiana, although we cannot confirm this result without testing mating type of all the isolates. This opens the question whether the similarity of Phytophthora communities in the soils of tropical areas is due to cryptic speciation and/or to human-mediated invasions.

Structure of the Phytophthora communities

The quantity of Phytophthora retrieved on bait leaves showed differences according to forest "environment x host family" interaction, with some families having especially high numbers of lesions on bait leaves in the TF environment. Owing to the large dominance of the P. heveae complex, this result probably refers mostly to P. heveae-like species. The fact that Oomycete mycelial growth is severely impaired in sandy soils compared to loamy soils [START_REF] Lumsden | A Nylon Fabric Technique for Studying the Ecology of Pythium aphanidermatum and Other Fungi in Soil[END_REF] coupled with studies showing that many Phytophthora do not develop well in low pH soils [START_REF] Jung | Involvement of soilborne Phytophthora species in Central European oak decline and the effect of site factors on the disease[END_REF] could explain the low quantity in white sand soils for most families. However, it is more surprising that seasonally flooded soils were not more favourable, as high hygrometry is generally favourable to the multiplication of Phytophthora species [START_REF] Erwin | Phytophthora Diseases Worldwide[END_REF], especially since higher Phytophthora diversity was observed in this environment. The sampling season is probably crucial: the soils were sampled in the two studied locations during very wet periods and thus the situation was very favourable also in TF environment. Still, all sampling was achieved in a relatively short timeframe in each location, rendering unlikely any bias between environments induced by the climatic conditions under which samples were collected.

A host plant effect at the family level clearly shaped the composition of Phytophthora community. However, no Phytophthora species were confined to specific families, providing little evidence for host specialization in this forest. Instead, Phytophthora species displayed host preference rather than strict specificity. As illustrated by several studies [START_REF] Arnold | Diversity and host range of foliar fungal endophytes: are tropical leaves biodiversity hotspots?[END_REF][START_REF] Dickie | Host preference, niches and fungal diversity[END_REF][START_REF] Ishida | Host effects on ectomycorrhizal fungal communities: insight from eight host species in mixed conifer-broadleaf forests[END_REF][START_REF] Tedersoo | Strong host preference of ectomycorrhizal fungi in a Tasmanian wet sclerophyll forest as revealed by DNA barcoding and taxon-specific primers[END_REF][START_REF] Schimann | Diversity and Structure of Fungal Communities in Neotropical Rainforest Soils: The Effect of Host Recurrence[END_REF], fungal communities may be influenced at different taxonomic levels of hosts. However, host taxonomic family effect has been shown to exist for two different fungal ecological groups: tropical epiphytic fungi [START_REF] Kembel | Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities[END_REF] and temperate ectomycorrhizal fungi [START_REF] Tedersoo | Phylogenetic relationships among host plants explain differences in fungal species richness and community composition in ectomycorrhizal symbiosis[END_REF]. Moreover, foliar plant pathogens do exhibit a phylogenetic signal in Neotropical forests [START_REF] Gilbert | Phylogenetic signal in plant pathogen-host range[END_REF].

However, because Phytophthora inoculum (motile zoospores) is known to be disseminated by stream and run-off from ground surfaces, sudden and heavy rainfall in tropical climates may be favourable to a rapid dissemination within both sites studied, therefore homogenising Phytophthora inoculum throughout local forests and blurring host taxonomy effects. Studying fine roots communities, instead of soil communities, could be an alternative method to detect host-specific Phytophthora species. Indeed, Khaliq et al., 2018, retrieved 25 different Phytophthora species on fine roots in urban parks environment, compared to nearly half that amount (13) in soil.

The community we studied is dominated by large host spectrum species such as P. heveae-like, P. cinnamomi and P. castaneae-like. Although initially described in rubber tree plantations in south east Asia, P. heveae is mainly a known pathogen of fruits of avocado and cocoa [START_REF] Turner | Pod rot of cocoa in Malaysia caused by Phytophthora heveae[END_REF][START_REF] Ochoa-Ascensio | Phytophthora heveae causing basal rot of avocado fruit in Mexico[END_REF] while P. castaneae has been described as infecting chestnut trees [START_REF] Katsura | Two new species of Phytophthora causing damping-off of cucumber and trunk rot of chestnut[END_REF]. However, in natural environments, they are mostly described as present in soils without causing symptoms to neighbouring trees [START_REF] Ko | The possible origin and relation of Phytophthora katsurae and P. heveae, discovered in a protected natural forest in Taiwan[END_REF][START_REF] Zeng | A Survey of Phytophthora Species on Hainan Island of South China[END_REF][START_REF] Jung | Diversity of Phytophthora species in natural ecosystems of Taiwan and association with disease symptoms[END_REF]. The fact that generalists dominate fungal communities in Neotropical forests has already been described [START_REF] Ferrer | Effect of tree host species on fungal community composition in a tropical rain forest in Panama: Fungal community composition in Panama[END_REF][START_REF] Schimann | Diversity and Structure of Fungal Communities in Neotropical Rainforest Soils: The Effect of Host Recurrence[END_REF]. Specialists may therefore be represented by very rare species.

Our study revealed that Phytophthora does not appear to be a promising agent of negative density dependence and Janzen-Connell effects in these French Guianan forests. However, only temporal demographic data, including long-term survival analysis of tropical seedlings, will ultimately arbitrate this result [START_REF] Gilbert | Effects of seedling size, El Nino drought, seedling density, and distance to nearest conspecific adult on 6-year survival of Ocotea whitei seedlings in Panama[END_REF]. Alternatively, other groups of plant pathogens, such as fungi, could play the prominent role in these forests. We suggest that a general survey of plant pathogens across phyla and in different Neo-tropical forests, would represent an ambitious yet extremely meritorious initiative.

Experimental Procedures

Sampling and location description

Soil was sampled in plots located in two regions, in coastal French Guiana, 137 km apart from each other (Fig. 1), Laussat region (5.48° N, 53.60° W, 4 plots) in mid-April 2015 and in the "Centre Spatial Guyanais" (abbreviated as CSG, 5.22° N, 52.84° W, 6 plots) in February and March 2015. These plots belonged to the three main forest environments in French Guyana: white sand (WS, 1 plot in Laussat and 2 in CSG), terra firme (TF, 2 plots in Laussat 2 in CSG), and seasonally flooded forests (SF, 1 plot in Laussat and 2 in CSG). Each plot was 10.000 m2. These three types of environments are described in [START_REF] Randall | A comparison of the forest soils in the Peruvian Amazon: Terra firme, palm, white sand and igap[END_REF]. The sample locations were relatively undisturbed. In each plot, 50 to 60 saplings (2-3 m in height) belonging to ten taxonomic families were selected for sampling (two to twelve individuals per family). The sampled saplings did not present any visible symptoms of disease. The host families were Burseraceae, Chrysobalanaceae, Fabaceae, Lauraceae, Lecythidaceae, Malvaceae, Moraceae, Myrtaceae, Rubiaceae and Sapotaceae. Focal families were selected for their relatively large distribution across Amazonian forests within the targeted environment types and the breadth of Angiosperm diversity they represent, as they belong to three major Angiosperms clades: the Asterids, Rosids and Magnoliids (see Fig. 5 for their repartition into these clades). Three soil samples 5-10 cm deep were collected at 15-40 cm from the stem collar after removing the litter at the base of each sapling. In each plot, soil samples collected from saplings belonging to the same family were then pooled. As all ten tree families could not be found in all ten plots, the number of pooled soil samples was 93.

Baiting and isolation

For each soil sample, two 1 l plastic containers filled with 200 mL of homogenised soil submerged by 500 mL of distilled water were prepared for baiting. In one container, three leaves of Rhododendron 'Cunningham's White' were placed floating upon the water, while the other received one leaf each of Cassava (Manihot esculenta) or Avocado (Persea americana). Care was taken to select flat leaves so to have homogeneous contact between the bait and the water. After three days of incubation at 20-22 °C, bait leaves were collected and weighed. The number of lesions was quantified on Rhododendron leaves only, because lesions became immediately too large and confluent on Cassava and Avocado leaves, preventing accurate quantification of infections. For each combination of bait leaf x soil sample, up to 10 lesions were cut and placed in Petri dishes containing a Phytophthora selective medium (V8 juice agar complemented with 10 mg L-1 pimaricine, 250 mg L-1 ampicilline, 10 mg L -1 rifampicine, 67 mg L-1 hymexazol, 15 mg L-1 benomyl). After 48 hours incubation in the dark at 25°C, mycelium was transferred onto new Petri dishes containing the same medium previously described for up to 5 putative Phytophthora distinct colonies per soil sample per bait leaf combination, preferentially selecting isolates with different morphology. After an additional 48-72 hours period of incubation, the mycelium was collected for DNA extraction, using the DNeasy 96 Plant kit (Qiagen) according to the manufacturer's instruction. PCR products were amplified from this genomic DNA using the ITS6-ITS4 primer pair targeting the ITS region, using the PCR protocol from [START_REF] Cooke | A Molecular Phylogeny of Phytophthora and Related Oomycetes[END_REF], and the YPh1F-YPh2R primer pair targeting RAS-Ypt gene, using the PCR protocol from [START_REF] Weir | A taxonomic revision of Phytophthora Clade 5 including two new species, Phytophthora agathidicida and P. cocois[END_REF]. Finally, 366 Phytophthora isolates were sequenced in both directions with the Sanger method. The different steps of the sampling, baiting, isolation and molecular identification process are reported in Fig. 2.

Additionally, as P. cinnamomi is a very important invasive species found worldwide, usually present as only one of the mating types [START_REF] Erwin | Phytophthora Diseases Worldwide[END_REF], the P. cinnamomi isolates retrieved from our samples were further characterized by determining their mating type. They were paired with A1 and A2 mating types of P. cinnamomi isolates from a reference collection, using the method described by [START_REF] Zentmyer | Stimulation of sexual reproduction in the A2 mating type of Phytophthora cinnamomi by a substance in avocado roots[END_REF].

Phylogenetic analysis of isolates

As clade 5 isolates (phylogenetically close to P. castaneae and P. heveae) were the most frequent in our study, twenty of these isolates were selected for further investigation of their genetic diversity. We selected these isolates to maximize their ITS sequence diversity. We then sequenced for each one the RAS-YPT gene and three additional genetic markers, Cox, Enolase and NADH genes (see [START_REF] Weir | A taxonomic revision of Phytophthora Clade 5 including two new species, Phytophthora agathidicida and P. cocois[END_REF] for the method used). Multiple sequence alignments for each gene were made using Seaview 4.0 [START_REF] Gouy | SeaView Version 4: A Multiplatform Graphical User Interface for Sequence Alignment and Phylogenetic Tree Building[END_REF]. Bayesian inference (BI) was used to reconstruct the phylogenies using MrBayes v. 3.2.2 [START_REF] Ronquist | MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space[END_REF] for the two categories of sequences: first, a concatenation of the mitochondrial markers (Cox and NADH), and secondly another concatenation of the nuclear markers (ITS, RAS-Ypt and Enolase). Nuclear genes showed congruent phylogenies, and so did mitochondrial genes. However, the phylogenies were not congruent between nuclear and mitochondrial genes, so we did not create a tree based on all five genes. Sequences of clade-5 Phytophthora from Weir et al. were used as references, and in both trees, P. multivora was used as outgroup. Two P. cinnamomi isolates (clade-7) from Laussat and two from CSG were also randomly selected and sequenced for the same markers.

Metabarcoding and Illumina Miseq sequencing

As detailed in Figure 1, two sets of DNA samples were created by extracting DNA from either (1) the 93 soil samples or (2) the 264 bait leaves corresponding to the three different bait species (Rhododendron, Avocado and Cassava, i.e. 93 x 3 bait leaves, minus 15 leaves showing no infection and discarded for further analysis). Soil samples were air-dried and sieved (2 mm mesh size). DNA extraction was performed using MP Biomedical Fast DNA™ SPIN Kit for soil on 500 mg of dried soil according to the manufacturer's instructions. Bait leaves were dried and ground using liquid nitrogen, and DNA of 500 mg leaf samples was extracted using DNeasy 96 plant kits.

PCR products were amplified from these 357 DNA samples using a nested PCR approach: the primer pair oom18SITS7 was used for the first round and the primer pair 18Ph2F/5.8-1R for the second round. The PCR protocol and the primer sequences are detailed in [START_REF] Legeay | Comparison and validation of Oomycetes metabarcoding primers for Phytophthora high throughput sequencing[END_REF]. In order to detect potential contaminants, fifteen control samples, consisting of pure distilled water added to the PCR mix, were included in the same PCR plates. The multiplexing and the Illumina MiSeq sequencing (half-run) were done by the GeT-PlaGe platform of Toulouse (http://get.genotoul.fr/).

Bioinformatics analysis

Sequences were sorted and trimmed according to their quality using Mothur [START_REF] Schloss | Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities[END_REF] and Usearch [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF]. Unfortunately, the quality of R1 and R2 reads was medium and the depth of sequencing was very heterogeneous between the samples. The R2 reads were finally retained because they covered the ITS1 nucleotide polymorphism region. The reads were then dereplicated to remove duplicated sequences and sorted by decreasing abundance, and the singletons were discarded using the sortbysize command of Usearch. Sequences were then clustered together to make consensus mOTUs with 99% similarity threshold, using cluster_otus command of Usearch. Generated mOTUs were taxonomically assigned using the BLAST algorithm against a database extracted from the NCBI database, containing all sequences assigned to Oomycetes with the "ITS "and "Internal Transcribed Spacer" keywords in their definitions. OTUs assigned to the same species were merged according to their taxonomic identity at the species level. Given the heterogeneity of the sequencing depth, we defined a compromise between the maximum number of samples and the minimum number of reads in each sample to cover the optimal diversity of Phytophthora. In addition, because the Sanger sampling has much less depth than metabarcoding, and because Phytophthora diversity is known to be relatively low, we chose to submit the metabarcoding library to processes of decreasing random subsampling. As illustrated in Figure S2, we could have kept only a hundred samples (n=120) with a sequencing depth > 1000 reads (Fig. S2, insert D). However, compared to Sanger sequencing data, this depth of sequencing does not allow the identification of additional Phytophthora species. Consecutively, we chose to normalize the samples by subsampling 72 reads because this was the number of reads which maximized the quantity of rare species retrieved in all samples (Fig. S2, insert B). Indeed, this Phytophthora diversity was identical to that observed with 1000 reads per sample from 120 samples. Due to quantitative distortions using these amplification and sequencing methods [START_REF] Legeay | Comparison and validation of Oomycetes metabarcoding primers for Phytophthora high throughput sequencing[END_REF], sequencing results were only analyzed qualitatively (i.e. presence or absence of species). ITS sequences were deposited in GenBank under the accession numbers MH938206 -MH938223. The sequences of RAS-Ypt, Enolase, Cox and NADH regions are available in the DRYAD database (http://datadryad.org/). DRYAD entry doi:XXXXX. The Miseq raw data were deposited in the Sequence Read Archive (http://www.ncbi.nlm nih.gov/sra) as SUB5066167 (temporary access number).

Data analysis

The efficiency of isolation from baits was studied by analysing the likelihood that a retrieved isolate was a Phytophthora species using a mixed logistic regression with a logit link, taking into account the bait leaf species, the environment, the host plant family and the location (glmer function of R). The plot was added as a random factor.

The number of lesions per bait leaves was analysed as a surrogate for the quantity of Phytophthora inoculum in soil. Effects of the different factors, host plant family, environment and location, on the number of lesions per bait leaf weight were tested with a generalized linear model using a Poisson distribution and a log link, with the medium leaf weight as offset (glmer function of R). The plot was added as a random factor. Differences in the composition of Phytophthora communities between the different methods, environments, locations and host families were tested by Permutational Multivariate Analysis of Variance (PERMANOVA) using the adonis function (number of permutations = 500) in the vegan package of R [START_REF] Oksanen | vegan: community ecology package. R package version 2[END_REF]. As all information on Phytophthora species composition could not be used in the metabarcoding data, in particular on P. heveae and P. castaneae complexes, a second analysis was performed using only the data from the baiting/isolation study. To further characterize the diversity of communities in the different environment / families, we computed the Shannon diversity index for each host family and each "environment x location" level using diversity function from the vegan package.

Figure 3 .

 3 Figure 3. Number of Phytophthora infection spots on Rhododendron leaves per leaf weight. Three forest environments have been studied: TF (terra firma), SF (seasonally flooded), WS (white sand).

Figure 5 .

 5 Figure 5. Proportion (%) of samples in which each of the Phytophthora taxa was retrieved by the baiting / isolation method, for (A) the ten sampled plant families and (B) the three environment types at both locations. One sample can contain several Phytophthora taxa.A: A significant difference was detected between families (p=0.004) but not between higher clades (p= 0.166). B: There was a significant difference between the two locations Laussat and CSG (p= 0.001) and between environment types (p= 0.017). TF=Terra firma. SF=seasonally flooded. WS = white sand.

Figure 1 .

 1 Figure 1. Sampling sites of the French Guiana experiment. A: Map of French Guiana showing the locations sampled for this study (B=Laussat ; C=CSG). B and C : Maps of Laussat (left) and CSG (right) showing the sampled plots as red dots. "Plateau" = firma environment.

Acknowledgements

We gratefully acknowledge E. Morin for her assistance in bioinformatics advices and A. Gillet for her technical support. JL holds a PhD fellowship awarded by the "Agence Nationale de la Recherche" (ANR) as part of the ANR Blanc program NEBEDIV to CB, MB and HS (ANR-13-BSV7-0009) and the "Institut National de la Recherche Agronomique" (Metaprogramme MEM). UMR IAM and this work were also supported by a grant overseen by the French National Research Agency as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). The UMR EcoFoG is supported by the French National Research Agency as part of the "Investissements d'Avenir" program (Lab of Excellence CEBA, ref. .


PhD. (orcid Serge Mordon

New optical sources for interstitial and metronomic photodynamic therapy

 In contrast to conventional optical fibers, which must be removed from the body soon after use, the biodegradable and biocompatible light sources may be used for longterm light delivery and need to be removed as they are gradually resorbed by the tissue. This letter aims to present two light sources which fulfill most of the requirements for iPDT and possibly for mPDT.

Introduction

Multiple clinical studies have shown that interstitial photodynamic therapy (iPDT) is a promising modality in the treatment of cancerous tumors in prostate, pancreas, head and neck cancer and brain. The laser fibers are into the target tissue inserted via needles, or placed in catheters [START_REF] Shafirstein | Interstitial Photodynamic Therapy-A Focused Review[END_REF] . However, the transport distance of light in biological tissues is limited by scattering and absorption. Practical therapeutic penetration depth is 0.1-1 mm for visible light in 400-600 nm and 2-3 mm for near infrared light in 700-1300 nm for most biological tissues. The fluence rate of therapeutic light must limited to prevent undesirable photothermal damage of tissues. The rate of oxygen consumption by the PDT process and the re-oxygenation rate of tissues may be also be an important consideration in deciding on fluence rate. Depending on photosensitiser and treatment site, lower fluence rates can also reduce pain problems. Consequently, when treating a large tumor, numerous lasers fibers must be inserted in the tumor. Moreover, except maybe for the brain, interstitial fibers cannot be left in tissue for a long period time. However, several studies have demonstrated that metronomic PDT (mPDT) based on continuous low rate delivery of photosensitizer and light, induces an enhanced induction of cell apoptosis as compared with a conventional PDT regimen [START_REF] Davies | Tetherless fiber-coupled optical sources for extended metronomic photodynamic therapy[END_REF][START_REF] Guo | Low-fluence rate, long duration photodynamic therapy in glioma mouse model using organic light emitting diode (OLED)[END_REF] . Thus, mPDT could benefit for light sources placed inside the tumor tissue and emitting for several hours or days.

Recently, several teams have elaborated innovative implantable and biodegradable light sources that can be used for iPDT and mPDT in particular. In contrast to conventional optical fibers, which must be removed from the body soon after use, the biodegradable and biocompatible light sources may be used for long-term light delivery and need not be removed as they are gradually resorbed by the tissue. This letter aims to present two light sources which fulfill most of the requirements for iPDT and possibly for mPDT.

Bioabsorbable polymer optical waveguide

The first innovative light emitting light source was developed by Nizamoglu et al. [START_REF] Nizamoglu | Bioabsorbable polymer optical waveguides for deep-tissue photomedicine[END_REF] .It is made of transparent and flexible poly(L-lactic acid) biopolymer. Polymers offer a wide range of biodegradation half-times, from 1 min to over a year [START_REF] Ratner | Biomaterials Science: an Introduction to Materials in Medicine[END_REF] . The relative amount of GA (Glycolide acid) in PLA (poly(L-lactic acid)) increases the biodegradation rate in PLGA (poly (D,L-lactideco-glycolide). Besides, lactide and glycolide-based copolymers are degraded by ester hydrolysis and break into monomers, which are further degraded and resorbed in the body with minimal systemic toxicity. Therefore, these polymers are chosen for making a resorbable light source. The solid waveguide is achieved by melt-pressing technique. Its dimensions are obtained by precise laser cutting. Consequently, this waveguide can be easily shaped.

Nizamoglu et al designed a waveguide similar to a trident to perform photochemical tissue bonding. In this application, a standard multimode optical fiber (200 µm core, 0.48 NA) is bonded to the waveguide using an optical adhesive. This optical fiber is connected to a 532nm laser. Tests performed at 1W and 15 minutes duration demonstrated the efficiency of this new device (see figure 1). Long term evaluation of this waveguide was not performed and biodegradability and toxicity were not evaluated. However, this new device is very promising due to relative ease of manufacture and ability to deliver substantial light intensities. Near-infrared rechargeable "optical battery" Another very innovative approach has been recently proposed by Hu et al [START_REF] Hu | Near-infrared rechargeable "optical battery" implant for irradiation-free photodynamic therapy[END_REF] . This team has developed a near infrared (NIR) rechargeable "optical battery" implant for irradiation-free continuous PDT. By combining the NaYF4:25%Yb,0.5%Tm upconversion materials with typical UV/blue upconversion emission (350/475 nm) and UV rechargeable SrAl2O4:2%Eu2þ,4%Dy3þ persistent phosphors (mass ratio, 1: 10), a green persistent luminescence at 520 nm can be activated by 980-nm NIR excitation due to the spectra overlapping between the upconversion emissions and absorption of persistent phosphors. The mixture was added into PDMS (biocompatible polydimethylsiloxane) and placed to special molds and degassed under vacuum for 30 min. The PDT optical battery can be obtained after solidify at 60 °C for 1 h and can be easily fabricated into any size and shape. Figure 2 shows several examples such as microneedles, wire, hollow tube or membrane (figure 2) After short (5 s) charging process by 980-nm laser, the "optical battery" implant can generate green persistent luminescence for 30 minutes. 2W/cm² is enough to full charge the persistent materials without any overheating of the surrounding tissues. Furthermore, the PDT "optical battery" implant can still be charged to continuously generate singlet oxygen even in deep tissue as high as 4mm because of the deep tissue penetration of the NIR charging source. Due to the facile preparation, flexible and bio-stability of PDMS (biocompatible polydimethylsiloxane), the irradiation-free PDT implant can be easily optimized in size and shape aiming at different nidus sites By adding the photosensitizer inside the battery, 1 O 2 can be continuously generated under persistent luminescence. Hu et al evaluated Rose Bengal which is known to be a potent photosensitizer [START_REF] Song | O2 and Ca(2+) fluxes as indicators of apoptosis induced by rose bengal-mediated photodynamic therapy in human oral squamous carcinoma cells[END_REF] . An experiment was performed on HT29 tumor implanted subcutaneously into mice (figure 3). Results clearly demonstrate that this implantable O2-generating PDT device greatly decreased the tumor volume of the mice after two NIR irradiation per day for 15 days.

Upconversion materials such NaYF4 have potential toxicity problems. However compared to upconversion nanoparticles, there is no IV injection [START_REF] Hamblin | Upconversion in photodynamic therapy: plumbing the depths[END_REF] . At last, the degradation of this optical battery should be studied carefully.

Conclusion

These innovative bioabsorbable waveguides and optical batteries may initiate a new paradigm and find widespread use in medicine. The use of polymers such as polydimethylsiloxane, PLA or PLGA,.. give a great adaptability of size and shape to specifically target a nidus site.

Further studies should evaluate the safety of the biodegradation process which will be required in order to approve these light sources on humans.

Figure 1 :

 1 Figure 1: Hybrid biopolymer-based waveguide bundle engineered for optimal light delivery to the tissue. Scale bars, 10mm (from ref 4)

Figure 2 :

 2 Figure 2: Photographs and 3D diagrams of the PDT implants with different shapes. i) PDT microneedles; ⅱ) PDT wire ⅲ) PDT hollow tube; ⅳ) PDT membrane (from ref 6).

Figure 3 :

 3 Figure 3: Schematic illustration of the NIR light rechargeable persistent luminescence activated PDT "optical battery" implant for tumor inhibition (GPM: green persistent luminescence materials; PSs: photosensitizers; UC: upconversion materials; TB: trapping band; VB: valence band). (from ref 6)


Agnès Grossel

Hocine Bourennane

Adeline Ayzac

Gilles Billen

Josette Garnier

Anun Martinez

Catherine Pasquier

Catherine Hénault

Riverine emissions of nitrous oxide in a cropland region in France

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

Riverine emissions of nitrous oxide in a cropland region in France

Agnès Grossel a , Hocine Bourennane a , Adeline Ayzac a , Gilles Billen b , Josette Garnier b , Anun Martinez b , Catherine Pasquier b , Catherine Hénault c Agriculture is the major source of nitrous oxide (N 2 O) emissions (4.1 Tg-N/year, IPCC) through direct emissions from soils with crops or animals and indirect ones after nitrogen transfer, downstream or downwind croplands. Indirect emissions are estimated to represent 22% of total anthropogenic emissions (UNEP 2013). One component of indirect emissions is riverine emissions but based on a small number of studies reporting very variable emission levels, estimations exhibit a very large uncertainty. Currently, the IPCC Tier I methodology for national inventories calculations proposes an emission factor for fresh water EF 5 =0.75% [0.05%-2.5%] of leached nitrogen emitted as N 2 O, one third being attributed to rivers (EF 5r ).

Due to the need for more observations of the riverine contribution in various climatic, land use and hydrogeological situations, the HydroGES project studies the spatial distribution of dissolved N 2 O and other nitrogen forms in a 3453 km 2 watershed (Haut-Loir, Centre region, France) with monthly sampling campaigns from February 2017 to December 2018. This site includes two contrasted hydrological regions: the western part (Perche and Faux-Perche region) with a dense tributary network and dominance of (sub)surface runoff and the eastern part (Beauce region) with a deep groundwater system.

HydroGES wants to provide estimation of riverine N 2 O emission with Tier 1 (calculation based on EF 5r ), Tier 2 (statistical modelling based on measured N 2 O emissions) and Tier 3 (Riverstrahler mecanistic modelling) at watershed scale in intensive cropland French regions. First results show a mean dissolved N 2 O concentration of 1.25 µg-N L -1 . In Beauce region, the ratio N 2 O-N/NO 3 -N is rather constant between winter and summer (0.023% -0.027%) while in Faux-Perche, a clear seasonal pattern is visible (0.009% -0.030%). It suggests thus that EF 5r are one order of magnitude below the IPCC proposed factor.

  a : UR Science du Sol, INRA, 45075 Orléans, France b : UPMC -UMR METIS, Paris, France c : UMR Agroécologie, INRA, Dijon, France

UNEP 2013 :

 2013 Bouwman, Lex, et al. Drawing down N 2 O to protect climate and the ozone layer. A UNEP Synthesis Report. United Nations Environment Programme (UNEP), 2013.


Partula V, Lecuyer L, Srour B, Bachmann P, Touillaud M, Druesne-Pecollo N, Galan P, Hercberg S, Cohen P, Hoarau H Zelek L Latino-Martel

P Gonzalez

Sociodemographic and economic factors are essential determinants of weight gain between before and after cancer diagnosis: Results from the prospective population-based NutriNet-Santé cohort

Body: Background: While many cancer patients are affected by weight loss, others tend to gain weight, which may impact prognosis and risk of recurrence and of second cancer. The aim of this prospective study was to investigate weight variation between before and after cancer diagnosis and socio-demographic, economic, lifestyle and clinical factors associated with moderate-to-severe weight gain. Methods: 1051 incident cases of first primary cancer were diagnosed in the NutriNet-Santé cohort between 2009 and 2015. Weight was prospectively collected every 6 months since subjects' inclusion (i.e. an average of 2y before diagnosis). Mean weights before and after cancer diagnosis were compared with paired Student's t-test. Factors associated with moderate-to-severe weight gain (≥5% of initial weight) were investigated by multivariable logistic regression. Results: Weight loss was observed in men (-3.54kg in those who lost weight, p=0.0002) and in colorectal cancer patients (-3.94kg, p=0.0012). Weight gain was observed in breast and skin cancers (2.83kg, p=0.047, and 2.96kg, p=0.03 respectively). Women (OR=1. 99[1.18-3.35]), younger patients (OR=1.78[1.05-3.03]), those with lower education (OR=2.17[1.07-4.37]), those with excess weight before diagnosis (OR=1.53[1.02-2.30]) and those who stopped smoking after diagnosis (OR=4.60[2.06-10.25]) were more likely to experience moderate-to-severe weight gain. In breast cancer patients, induced menopause was associated with weight gain (OR=4.12[1.76-9.67]), but no association was detected for tumor characteristics or treatments. Conclusion: This large prospective cohort provided original results on weight variation between before and after cancer diagnosis, highlighting different weight trajectories. Socio-demographic and economic factors appeared to strongly influence the risk of weight gain, illustrating social inequalities in health.


domain: sdv sde

text:
Laurence Denaix

come

Evolution des transferts sols-solution de pesticides organiques et inorganiques dans des sols viticoles

pendant une année de culture

Evolution des transferts sols-solution de pesticides organiques et inorganiques dans des sols viticoles pendant une année de culture Manon Pierdet 1,2 , Juliette Gaillard 1 , Chloé Quenot 2 , Sylvie Bussière 2 , Cécile Coriou 2 , Thierry Robert 2 , Karyn Le Menach 1 , Patrick Pardon 1 , Marie-Hélène Dévier 1, Hélène Budzinski 1 et Laurence Denaix 2

(1) Université de Bordeaux-CNRS, EPOC-LPTC, UMR 5850, Talence, France

( 2 )

 2 INRA de Bordeaux-ISPA, UMR 1391, Villenave d'Ornon, France Remerciements : Ce travail a été réalisé dans le cadre du projet PhytoCOTE avec le soutien financier de l'ANR dans le cadre du programme investissement d'avenir au sein du Laboratoire d'Excellence COTE (ANR-10-labx-45), de la Région Nouvelle-Aquitaine et de l'Union européenne (projet CPER A2E). L'Europe s'engage en Nouvelle-Aquitaine avec le fonds européen de développement régional (FEDER). La propagation de bio-agresseurs et l'augmentation de la population ont conduit à une utilisation intensive de pesticides. En viticulture, ces pesticides sont organiques (ex: fongicides, herbicides, insecticides) et minéraux (ex: cuivre -> bouillie bordelaise (depuis 1882)). Ces apports réguliers peuvent conduire à une contamination des écosystèmes et affecter la faune et la flore. Différents processus dans les sols jouent un rôle dans la rétention et le transfert des pesticides. Pour évaluer la disponibilité et la dynamique des transferts sol-solution, différentes méthodes sont utilisées (extraction, échantillonneurs passifs) mais elles sont généralement utilisées dans des conditions standardisées au laboratoire sur sol préalablement prélevé. Nous avons cherché à suivre la solution du sol directement sur le terrain afin de tester si la disponibilité des contaminants variait au cours de l'année, en fonction des périodes de traitements ou des paramètres climatiques. viticulteur, -pédologie variée (luvisol/podzosol), -concentrations en cuivre allant de 15 à 197 mg/kg, grande diversité de pesticides organiques dans les sols 031solution + DGT Sols secs -> pas de solution Colloque de la Société Française d'Ecotoxicologie Fondamentale et Appliquée SEFA -Lyon, les 24 et 25 juin 2019 3. Extraction et dosages : Après un premier screening de 205 molécules et 4 métaux au niveau de l'horizon 0-15 des sols, 64 pesticides ont été recherchéstous les 15 jours pendant 1 an si les conditions d'humidité le permettent (de mars 2018 à mars 2019) : DGT installées dans le sol pendant 24 h et extraction de solution de sol par bougies Produits épandus : pic de concentration après un épandage Variation de concentration sans lien avec l'épandage Hypothèse : Reprise de la minéralisation en fin d'hiver : lorsque les températures remontent, les communautés microbiennes reprennent la minéralisation de la matière organique qui pourrait permettre un passage en solution plus rapide des pesticides complexés à sa surface Valeurs pouvant engendrer de la rhizotoxicité pour le cuivre (Garcia et al., 2004 ; Juang et al., 2012)  Forte évolution au cours de l'année  Evolution variable selon les parcelles  Pic de contamination après épandage  Concentration variables selon les ont été quantifiées sur 63 recherchées ainsi que le cuivre. Il y a des variations importantes entre les parcelles mais des variations encore plus importantes au cours de l'année pour une même parcelle • L'épandage peut expliquer les pics de contamination en solution, en cours de saison de traitement. Il est possible de retrouver des métabolites de molécules interdites depuis plusieurs années à des concentrations souvent plus importantes que celles de molécules actuelles. Les concentrations observées pour le cuivre pourraient engendrer de la rhizotoxicité. • Un fort passage en solution est observé pour le cuivre et certaines molécules organiques en fin d'hiver lorsque la température remonte.


A Vinet

H Leclerc

F Marquis

F Phocas

Genetic analysis of calf vitality, survival and disease resistance in Charolais beef cattle

Keywords: diarrhea, health, mortality, navel illness, umbilical infection, vigor

The objective of the present study was to investigate calf health and survival. Data of 2740 Charolais calves, born in 16 French farms and observed from birth until 30 d of age were analyzed. Both direct and maternal genetic parameters were estimated for vitality of the calf at birth (NV), survival at 30d (Surv), umbilical infections and diarrhea for 2 age slots: 0-5d and 6-20d (respectively, Umb1, Diar1, Umb2, Diar2). Direct and maternal heritabilities ranged respectively from 0 to 0.081 and from 0 to 0.096. Maternal genetic effects were clearly more important to explain health performance than direct genetic effects for Surv, Diar1 and Umb2. Genetic correlations were estimated with large standard errors and varied markedly between traits varied strongly (from 0 to 1 in absolute values) depending on traits, age slot for a given trait and the nature of the genetic effects considered, direct or maternal. Therefore, the question of the priority arises for the breeding objective because all health traits will be difficult to improve simultaneously in Charolais cattle.

Introduction

Calfhood diseases have a substantial economic impact on beef and dairy farms, and most cases occurred in calves less than 1 month old. Literature on genetics of calf health is scarce, especially on beef calf health. Because French breeders were interested in improving viability of Charolais beef calves, 16 selected herds from a collaborative network of 75 herds were engaged in recording of various calf health performance from birth to one month of age during two successive birth years: from August 2013 to July 2015. The aim of the study was to estimate genetic parameters for calf health traits in order to answer two key questions: Is it feasible to directly select calf health traits? Can we make an indirect selection of calf health through more general performance such as survival or vitality of the calf at birth?

Material and methods

The analysed data came from 16 Charolais herds, including the two INRA experimental Charolais farms located at Bourges in Berry and at Pin-aux-Haras in Normandy, that were dedicated to the on-farm DEGERAM project managed by the French Charolais breeding society to develop genomic selection on new traits. Selected herds had a minimum of 3 health events per herd-year and were genetically connected to each other by the use of at least one artificial insemination sire with progenies in a minimum of three herds. 2740 calves bred by 2044 dams and 252 bulls from 16 connected herds were included in the final data set. Performance recording included vitality of the calf at birth (NV) and all health events from birth to one month of age and was directly recorded by the breeders. NV was scored from 1 (very vigorous calf reaching the udder within the first hour after birth) to 4 (assisted calf who needs help to stand and reach the udder) with intermediate scores of 2 (vigorous calf reaching the udder between 1 and 3 h after the birth) and 3 (weak calf needing more than 4 hours to reach the udder). For each health event, the breeder was requested to record the date of occurrence and the suspected disease. Health events occurring at different time slots were defined as binary disease traits (0=no disease event; 1=at least one disease event during the considered time slot) based on whether or not the calf had at least one health event recorded within the considered period. From this database, genetic analyses were conducted on the two most frequent diseases: umbilical infections and diarrhea occurring either between 0 and 5 d of age (Umb1, Diar1) or between 6 and 20 d of age (Umb2, Diar2). The time slots were defined because they were the ones that allow to detect the most significant genetic variations and because of prior knowledge about the different causes of infections for diarrhea depending on the calf age (Cho & Yoon, 2014;Gruenberg, 2016). Survival (Surv) was defined as a binary trait (0=dead; 1=alive), based on whether or not the calf was still alive at 30 d of age. This information was extracted from the French Charolais national database used for the on-farm genetic evaluation for all new-born calves, after excluding calves from twin birth. A pedigree file composed of 6530 individuals and 3 generations of ancestors for the 2740 calves with performance was considered in the genetic analysis. Mixed linear single and multi-trait BLUP animal models were used to estimate direct and maternal genetic (co)variances, common maternal and residual environmental (co)variances of each trait using ASREML software (Gilmour et al., 1995). Covariance between direct and maternal genetic effects was neither considered within trait nor between traits. The fixed effects identified for each trait were a combination of age and parity of the dam and the calf contemporary group, defined as a combination of herd, birth year and birth season. In addition, a sex effect was significant for NV, Umb1, Umb2 and Surv and calf born as twin was only a significant effect for NV.

Results and discussion

Table 1 gives the phenotypic means and the raw standard deviations for the 6 traits considered in the study. Calf survival at 30 days (96%) was above the national average (93%) for Charolais breed (Leclerc et al., 2016). Concerning NV, 62.28% of calves were very vigorous, 32.19% vigorous, 4.04% weak and 1.49% assisted at birth. No health event was recorded for 1947 calves of the 2740 calves of the dataset while 1052 health events were recorded for the remaining 793 calves. The total incidence risks (number of calves treated for the first time during the considered slot divided by the number of calves eligible to be treated) of umbilical infections and diarrhea during the first month of life were about 6% and 17% respectively.

Estimates of genetic parameters and common maternal environmental (co)variances are presented in Tables 2 and3. Large standard errors were calculated for all parameters, and in particular for all estimates of genetic correlations. Although we found similar value for direct heritability of NV (8% vs 9%) as in Riley et al. (2004), these authors estimated a maternal heritability of 10% not in accordance with our current null estimate. In our study, a large proportion (nearly 17%) of phenotypic variance for NV was explained by maternal common environmental variance (Table 3). Concerning survival, we estimated a low direct heritability (2.6%) in the range of most values found in the literature (Fuerst-Waltl & Comment citer ce document : Vinet, A., Leclerc, H., Marquis, F., Phocas, F. ( 2018). Genetic analysis of calf vitality, survival and disease resistance in Charolais beef cattle. Presented at 11. World Congress on Genetics Applied to Livestock Production (WCGALP), Auckland, NZL (2018-02-11 -2018-02-16). Sorensen, 2010;Leclerc et al., 2016) and a maternal genetic heritability (9.6%) almost 4 times larger than the direct heritability (Table 2). Genetic effects involved in resistance to diarrhea seemed to depend on calf age: only maternal genetic effects were detected for early diarrhea (Diar1) whereas the two types of genetic effects, direct and maternal, were observed in later diarrhea (Diar2). In Holstein, Mahmoud et al. (2017) have estimated only direct genetic heritability for calf diarrhea with estimate of 6%. Significant larger proportion of phenotypic variance of Diar2 was explained by maternal common environment effects (8.7%, Table 3) rather than by any genetic effects (h²d=1.6% and h²m=2.4%, Table 2). As regards to umbilical infections (Table 2), the genetic effects involved in Umb1 are mainly direct genetic effects (h²d=8.1%) whereas genetic effects involved in later infections Umb2 are exclusively maternal genetic effects (h²m=7.9%). Such a result is unusual because in general maternal effects had more impact on performance at younger ages. No maternal common environmental variance was detected for navel illness at any stage. Our results are not fully in agreement with the only other estimate of direct heritability for umbilical infections of 14% in Holstein heifers from birth to 3 months of age (Henderson et al., 2011).

In terms of direct genetic correlations between Surv and NV, our results showed that the more vigorous at birth the calf is, the higher is the probability to survive at one month of age (Table 2). In consequence, a reduction of death loss may be envisioned by genetic improvement of NV, which has a significantly higher heritability than Surv. Direct genetic effects for Surv and NV were also strongly favourably correlated to direct genetic resistance to Diar2 (Table 2). In addition, correlations between maternal genetic effects for Surv and any maternal genetic disease trait were positive (correlation estimates ranging from 0.34 to 0.59, Table 2). Therefore selecting for any maternal genetic disease resistance trait may be not recommended to improve calf survival. However due to the limited data for estimation of genetic correlations, caution should be taken in interpreting all these results. Most maternal genetic correlations between disease resistance traits were highly unfavourable (Table 2) suggesting that selection to improve maternal genetic resistance to one kind of calf infection may severely affect the resistance to other infections. In view of these last counterintuitive results and the associated high standard errors, further analysis on large-scale datasets should confirm or infirm these results.

Conclusion

Our study revealed some significant genetic variability in beef calf health traits, in particular neonatal vitality, survival and umbilical infections. In addition, maternal genetic effects were clearly more important to explain health performance than direct genetic effects for survival at 30 days, early diarrhea and umbilical infections between 6 and 20 days of age. These first results for beef calf health genetics will have to be strengthened by large scale studies with several tens of thousands of phenotyped calves. In the current genomics era, the extent of genetic variation presents in calf health traits makes it possible to propose direct genomic selection on calfhood disease resistance if an adequate reference population is constituted and managed over time. This also implies that breeding goals should considered health traits not only directly through the use of health phenotypes, but also indirectly through the use of correlated traits such as calf survival or neonatal vitality.

Table 1 .

 1 Number of records, mean value (incidence in case of disease traits) and raw standard deviations for calf health traits.Comment citer ce document : Vinet, A., Leclerc, H., Marquis, F., Phocas, F. (2018). Genetic analysis of calf vitality, survival and disease resistance in Charolais beef cattle. Presented at 11. World Congress on Genetics Applied to Livestock Production (WCGALP), Auckland, NZL(2018-02-11 -2018-02-16).

		Surv a	NV b	Umb1 c	Umb2 d	Diar1 e	Diar2 f
	Number of records	2356	2423	2740	2740	2740	2740
	Mean value / incidence	96.31 % 1.447 pt	2.92 %	3.47 %	5.26 % 11.68 %
	Raw standard deviation 18.86 % 0.646 pt 16.84 % 18.30 % 22.32 % 32.12 %
	a Surv = survival at 30 d ; b NV = neonatal vitality scored from 1 (very vigorous -calf standing, walking and reaching for an udder within the first hour after birth) to 4 (assisted calf who requires help to stand and reach
	an udder); c Umb1 = umbilical infections occurring between 0 and 5 d of age; d Umb2 = umbilical infections occurring between 6 and 20 d of age; e Diar1 = diarrhea occurring between 0 and 5 d of age; f Diar2 =
	diarrhea occurring between 6 and 20 d of age.

Table 2 .

 2 Direct and maternal heritabilities respectively on the first and second lines of the diagonal, direct genetic correlations on the upper triangle and maternal genetic correlations on the lower triangle (standard errors are in brackets). NV = neonatal vitality scored from 1 (very vigorous -calf standing, walking and reaching for an udder within the first hour after birth) to 4 (assisted calf who requires help to stand and reach an udder); c Umb1 = umbilical infections occurring between 0 and 5 d of age; d Umb2 = umbilical infections occurring between 6 and 20 d of age; e Diar1 = diarrhea occurring between 0 and 5 d of age; f Diar2 = diarrhea occurring between 6 and 20 d of age.

		Surv a	NV b	Umb1 c	Umb2 d	Diar1 e	Diar2 f
	Surv	0.096 (0.04) 0.026 (0.03)	-0.53 (0.56) -0.32 (0.52)	-	-	-0.71 (1.01)
	NV	-	0 (ne) 0.078 (0.04)	0.27 (0.33)	-	-	0.999 (ne)
	Umb1 0.52 (0.50)	-	0.019 (0.02) 0.081 (0.04)	-	-	-0.07 (0.61)
	Umb2 0.34 (0.27)	-	-0.68 (0.87)	0.079 (0.02) 0 (ne)	-	-
	Diar1 0.48 (0.39)	-	0.49 (0.78) -0.68 (0.32)	0.048 (0.03) 0 (ne)	-
	Diar2 0.59 (0.53)	-	-0.999 (ne) -0.85 (0.49)	0.33 (0.52)	0.016 (0.02) 0.024 (0.02)

ne: not estimable a Surv = survival at 30 d ; b

Table 3 .

 3 Proportion of phenotypic variance due to maternal common environmental effect (on the diagonal) and correlations between maternal environmental effects (above the diagonal). Surv = survival at 30 d ; b NV = neonatal vitality scored from 1 (very vigorous -calf standing, walking and reaching for an udder within the first hour after birth) to 4 (assisted calf who requires help to stand and reach an udder); c Diar1 = diarrhea occurring between 0 and 5 d of age; d Diar2 = diarrhea occurring between 6 and 20 d of age.

		Surv a	NV b	Diar1 c	Diar2 d
	Surv	0.065 (0.06)	0.05 (0.33)	-0.85 (0.86)	0.03 (0.50)
	NV		0.166 (0.04)	0.69 (0.53)	0.05 (0.22)
	Diar1			0.044 (0.04)	0.999 (ne)
	Diar2				0.087 (0.04)

ne: not estimable a

Acknowledgements

The authors thank the European Regional Development Fund (ERDF), the French Burgundy and Limousin Regions, the two INRA experimental units (Bourges and Le Pin-au-Haras) and

Comment citer ce document : Vinet, A., Leclerc, H., Marquis, F., Phocas, F. ( 2018). Genetic analysis of calf vitality, survival and disease resistance in Charolais beef cattle. Presented at 11. World Congress on Genetics Applied to Livestock Production (WCGALP), Auckland, NZL (2018-02-11 -2018-02-16).

the 14 farmers for their funding and implication in the DEGERAM project. They also thank France Génétique Elevage that funded the MORPHE project for providing survival data.

List of References


Mathieu Arnal

Christèle Robert-Granié

Hélène Larroque

Classification of lactation curves on French dairy goats

Keywords: Lactation curve, lactation persistency, functional clustering, dairy goat Courbe de lactation, persistance laitière, classification de courbes, chèvre laitière

The objective was to achieve a typology of lactation curves and to analyze the influence of environmental factors on these curves. The data used consists of 2,245,628 monthly test-day records of milk on 213,534 Saanen and Alpine French goats. A principal component analysis (PCA) was performed on milk test-day records with R package fdapace. Three principal components were found: one related to the level of milk production, another to the persistency and a third to the shape in middle lactation. The first principal component was mainly explained by herd effect, milk yield estimated breeding value (EBV), parity and region. The second one was mainly explained by month of kidding, breed, age at kidding, dry period length, gestation stage, somatic cell score (SCS) EBV, and parity. The third one was mainly driven by the month of lactation. A classification was used on PCA lactation scores and 5 clusters were found.

I -Introduction

France is one of the world's leading countries in terms of goat milk production thanks to its herds with high genetic potential, composed mainly of two internationally recognized breeds (Alpine and Saanen). This production is carried out by 850,000 goats distributed into: 3,000 farms of 225 goats on average delivering their milk directly to dairies and producing 80% of the French volume, and 2900 farms of 79 goats on average processing part or all of their production [START_REF] Maigret | Chiffres clés 2016-Productions caprines[END_REF]. Every 4 to 5 weeks, a technician measures the dairy production of goats called test-day in farms that adhere to the official performance control. All these data are stored in the national database of the Genetic Information Treatment Center (CTIG) of the INRA.

The shape of the lactation curve is of interest for several reasons. First, for the same total production during a lactation, a goat with a "flat" curve is said to be persistent and will have a lower production level at the lactation peak. This is particularly interesting because a high level of production at the lactation peak can lead to an energy deficit of the goat due to its inability to ingest enough food to compensate for its large production. The goat then draws on its body reserves, which can lead to metabolic and reproductive problems [START_REF] Gipson | Lactation curves in dairy goats: a review[END_REF]. A persistent goat is able to be fed with basic forages and few concentrate [START_REF] Sölkner | A comparison of different measures of persistency with special respect to variation of test-day milk yields[END_REF], which is an advantage, in a context of new environmental policy measures. It is known that environmental factors influence the shape of the lactation curve [START_REF] Gipson | Lactation curves in dairy goats: a review[END_REF]. The objective of this study was to achieve a typology of lactation curve forms in the two main French dairy goat breeds (Alpine and Saanen) from a large dataset of milk production records from the French national database and to analyze the influence of different environmental factors on these curves.

II -Material and methods

Data

After data selection, measurements consist of 2,245,628 monthly test-day from 319 975 lactations of 213 534 French Saanen and Alpine goats collected between September 2008 and June 2012 in 910 French herds by the French official record program. The weights of morning and evening milk produced were added to calculate the daily milk yield of a goat. Lactations should last between 180 and 350 days to be conserved. Test-day records before the 7th and beyond 300 th day of lactation were not considered. Within a lactation, a goat was required to have at least 4 test-day records per lactation with the first one occurring before the 81 st day of lactation. Only goats from herds having at least 30 conserved lactations per year were kept. On average, a goat had 7 test-days per lactation and Alpine goats represented 60% of the data.

Analysis

All statistical analyses were completed using R software. A Principal Component Analysis (PCA) was performed on milk yield test-days, for each lactation, using the R package fdapace [START_REF] Dai | fdapace: Functional Data Analysis and Empirical Dynamics[END_REF]. This package was appropriated to our dataset: sparse data, irregular measurement stage, irregular number of measurements per lactation. Based on the principal component (PC) scores of the lactations, a cluster analysis was performed using the R package Rmixmod [START_REF] Langrognet | Rmixmod: Supervised, Unsupervised, Semi-Supervised Classification with MIXture MODelling[END_REF]. Rmixmod contains a set of functions to fit a mixture model of multivariate Gaussian or multinomial components to a dataset. The optimal number of clusters was researched between 2 and 5 clusters and the Bayesian Information Criterion (BIC) was used to determine it.

The effects of environmental factors on each principal component of PCA were tested with a linear Gaussian model. The fixed effects considered were: the breed, the French herd region, the parity, the age at kidding for the three first parturitions, the kidding month, the dry-period length before their last kidding, the gestation stage at 300 days after the last parturition, the genetic milk estimated breeding value (EBV) based on total lactation milk yield (kg), the somatic cell score (SCS) EBV (high SCS index are correlated with small SCS), with respectively 2, 3, 8, 5, 12, 5, 7, 4 and 5 levels. For each model, a factor was successively removed in order to study its relative impact on each PC using a Fisher test and the determination coefficient (R²). Leastsquare means (LS-means) were calculated for each fixed effect to interpret the correlation between the environmental factor and the PC. The influence of the herd on the PC scores was studied from the residuals of each linear Gaussian model.

III -Results and discussion

Principal Component Analysis and Classification

In average, goats produced 31.4 hg of daily milk during 283.4 days for a total milk yield of 964 kg on a lactation. The first 3 components explained 99.5% of the original variance: 81%, 15%, and 3.5% for PC1, PC2, and PC3 respectively. Only these three PC were studied due to the high percentage of variance explained. PC1 represented an indicator of the level of production, PC2 a persistency indicator, and PC3 an indicator of linearity deviation in the middle of lactation. [START_REF] Macciotta | Use of multivariate analysis to extract latent variables related to level of production and lactation persistency in dairy cattle[END_REF] performed a PCA on dairy cow correlation test-days milk yield matrix. They did not used all the lactation stage but they summarized the information by the test-day rang in the lactation. They obtained two PC: the first one representing the total lactation production and the second one the persistency. These results were in accordance with ours. Five clusters were obtained according to the BIC score. A greater number of clusters was not kept in order to maintain sufficient cluster sizes. Procedures were run several times on the whole dataset and the same clusters were found each time. The averages of daily milk production were plotted according to each cluster number (Figure 1). The cluster 2 was characterized by the lowest total level of milk production and a curve shape similar to the mean curve. The cluster 5 was characterized by a "flat" curve. The mean curves of clusters 1, 3 and 4 (nearly 30% of the data) were very different to the mean curve. Indeed, the cluster 3 had the higher level of milk production and the higher persistency. The cluster 1 had a very marked lactation peak. The cluster 4 was characterized by a "bombed" curve (between the 70 th and 200 th lactation stage). The mean curves showed a great coherence between two consecutive stages with smooth profiles. The PC3 contributed significantly to the diversity of the shape of curves highlighted and had led to build clusters 1 and 3.

Relationship between environmental factors and Principal Components

The milk EBV and the region were the most correlated effects with PC1 with a correlation equal to +0.51.The goats bred in South of France produced less than the goats in North-East which produced less than those in North-West. The breed, the gestation stage, the SCS EBV, the age at kidding, the kidding month and the length of dry period were correlated with PC2. The goats were more persistent when their gestations were less advanced at 300 days after their previous parturition, when they were dried during a short period before their last kidding, when they had a high SCS EBV. Saanen goats were more productive and more persistent than the Alpine goats.

The goats which kidded younger had a lower level of milk production but a higher persistency.

From the first to the fourth parity, the goats produced in average more milk but their lactations were less persistent at each parturition. After the fourth parity, they produced less and less milk and they were a little bit less persistent than in the fourth lactation. Lactations were less and less persistent when the goats kidded from January to June and were more persistent in the second part of the year. The kidding month was the most linked to the PC3 and presented a great variability with a maximal value in February and a minimum value in August. The effect of the kidding month reflects the effect the evolution of the photoperiod length 120 days later when the bulge of the curve is observed. The influence of herd on residuals of the linear Gaussian models showed important R², equal to 0.397, 0.178 and 0.162 for the 1 st PC, 2 nd PC and 3 rd PC respectively. [START_REF] Bouloc | Analyse de la forme de la courbe de lactation[END_REF] obtained similar results on French goats for the effects of herd, parity, breed, region and age at kidding. The effect of kidding month on persistency had already been highlighted by [START_REF] León | Characterization of the lactation curve in Murciano-Granadina dairy goats[END_REF] in Murciano-Granadina dairy goats. This effect could be linked to the duration of the day. The effect of the duration of the day on the lactation curve was also found by [START_REF] Delouis | Influence de la durée quotidienne d'éclairement sur la production laitière de la chèvre[END_REF]: an important exposure to artificial light affects the milk yield for all the lactation stage, until +33% in France. [START_REF] Knight | Milk production in concurrently pregnant and lactating goats mated out of season[END_REF] showed that the gestation induce a decrease of milk yield comparing to non-gestating goats. [START_REF] Caja | Omitting the dry-off period negatively affects colostrum and milk yield in dairy goats[END_REF] studied the impact of the dry-period length and they found that goats with no dry-period had a smaller peak and less milk during the than goat dried. The relationship between high SCS EBV and the persistent goats did not seem to be known. A hypothesis could be that goats with low SCS genetic level are more able to protect themselves against diseases allowing to maintain a high milk production. EBVs for type traits were studied and no effect on PCs was found. It was not possible to test here the effect of the number of kids whereas Leon et al (2012) showed that this factor affected the amount of milk and the shape of the lactation curve.

Analysis with more data on the animal health, alimentation system, weather,… would be interesting to better understand the large effect of the herd.

IV -Conclusions

This study, based on a large number of goats, allowed to characterize different shape of lactation curves and to analyze the influence of environmental factors on each cluster. Three principal components were found: one relating to the level of milk production, another to the persistency and a third to the shape in middle lactation. Five clusters were found with very different lactation curves. This work is a first step to model the lactation curves according to the environmental factors and to propose a genetic evaluation model to study the persistency.

Fig. 1 :

 1 Fig. 1: The average lactation curves for each cluster and the general mean curve (in black) onFrench dairy goat data. Each point corresponded to the average production of animals in the cluster, and for each cluster, the percentage of lactations was noted.