Comamonadaceae that is found ubiquitously in freshwater habitats. In this study, we report two novel phages, P26059A and P26059B, that were isolated from Lake Soyang in South Korea, and lytically infected bacterial strain IMCC26059, a member of the family Comamonadaceae. Morphological observations revealed that phages P26059A and P26059B belonged to the family Siphoviridae and Podoviridae, respectively. Of 12 bacterial strains tested, the two phages infected strain IMCC26059 only, showing a very narrow host range. The genomes of the two phages were different in length and highly distinct from each other with little sequence similarity. A comparison of the phage genome sequences and freshwater viral metagenomes showed that the phage populations represented by P26059A and P26059B exist in the environment with different distribution patterns. Presence of the phages in Lake Soyang and Lake Michigan also indicated a consistent lytic infection of the Comamonadaceae bacterium, which might control the population size of this bacterial group. Taken together, although the two phages shared a host strain, they showed completely distinctive characteristics from each other in morphological, genomic, and ecological analyses. Considering the abundance of the family Comamonadaceae in freshwater habitats and the rarity of phage isolates infecting this family, the two phages and their genomes in this study would be valuable resources for freshwater virus research.Bacteriophages of freshwater environments have not been well studied despite their numerical dominance and ecological importance. Currently, very few phages have been isolated for many abundant freshwater bacterial groups, especially for the family  Bacteriophages mostly replicate through host bacterial cell lysis and hence, phages not only participate in bacterial population control but also intervene nutrient metabolism and cycling. Phages also have a role in controlling bacterial genetic diversity by mediating horizontal gene transfer through unintentional carriage of host gene fragments while infecting one host after the other2. In the process of carrying host genome fragments within the phage capsid, some phages acquire auxiliary metabolic genes (AMGs) within their own genome that can be expressed and alter the cell metabolism upon phage infection and eventually benefit phage reproduction. AMGs involved in photosystem, glycolysis, and phosphorous, sulfur, and nitrogen cycling have been found in many phage genomes and viral metagenomes5.Bacteriophages (phages) are the smallest but the most abundant biological entities on earth6. Without the need of culturing both bacteria and phages, virome analysis provided access to large amounts of bacteriophage genomes in the environment. Through assembling virome reads, many studies reported novel and abundant bacteriophage genomic contigs in various environments9. These studies revealed that the analyses of genome (or genomic contig) sequences alone were not sufficient to provide information on the hosts or morphology of phages, which are the basic information required for phage classification. Although virome analyses disclosed large numbers of environmental viral sequences, the lack of independently isolated phages led to limited interpretation of the virome data. Therefore, virome approaches to study phages in the environment must be accompanied by the isolation and characterization of individual phages.Despite the diverse functions of phages in the environment, available genomic resources of environmental phages are rather limited in quantity and diversity. One of the major constraints in phage studies is that respective bacterial hosts need to be cultured first to isolate their infecting phages. However, only a small proportion of bacteria in nature can be cultured in laboratory settings, and even if they are successfully cultured, maintaining those strains are challenging. In order to overcome the culturability restrictions, viral metagenome (virome) was suggested as an approach to discover the unexplored diversity of bacteriophage genomesPuniceispirillum phages11, and surveying marine viral population through metagenome12. However, contrast to the marine environment, only a few studies have been performed in lakes, which include researches on freshwater cyanophages16. Only recently, the phages infecting the LD28 bacterial clade were cultured from an oligotrophic freshwater lake17, and metagenome-assembled genomes of the phages that are considered to infect the acI clade were retrieved from viromes18. Each lake across the globe is confined and independent from one another such that each has its own unique ecological values, regardless of their geographic locations and variation in climates and environments. Despite the large diversity in lakes, the major bacterial composition is very similar throughout lakes while the detailed community structure is different even in those under similar environmental conditions21, providing interesting aspects in freshwater microbial evolution and ecology. Thereby, extensive studies on individual freshwater bacterial groups have been performed by many researchers24. However, representatives of many abundant freshwater bacterial groups are still uncultured, along with their phages.Most of the studies on environmental bacteriophages have been subjected for marine phages, isolating the most abundant bacteriophages such as pelagiphages and Comamonadaceae of the class Betaproteobacteria25. The family Comamonadaceae is one of the dominant bacterial groups in freshwater environments and includes well-known freshwater bacterial genera such as Limnohabitans and Rhodoferax20. Despite the abundance and ubiquity of Comamonadaceae in freshwater habitats, phages infecting members of this family were rarely studied in freshwater habitats. To our knowledge, only one lytic phage that infects a strain of Rhodoferax, and three phages that infect Delftia of the family Comamonadaceae have been reported so far27. In this study, to obtain more freshwater phages that infect Comamonadaceae, lytic bacteriophages have been screened from Lake Soyang using the IMCC26059 strain as a host, which resulted in the successful isolation of two phages, P26059A and P26059B. Morphological analysis and whole genome sequencing were performed for the two phages. Subsequently, the distribution of the two phages in diverse freshwater environments was determined using the genomes of the phages and freshwater virome data, contributing to the understanding of freshwater phage populations and their diverse genomes.Lake Soyang, located in South Korea, is the largest artificial lake in Korea that is designated as a source water protection area. As a well conserved oligotrophic lake, Lake Soyang is inhabited by diverse bacterial lineages and phage groups. To study the microbial population dynamics of this lake, a number of bacterial strains and bacteriophages have been isolated including IMCC26059, a bacterial strain used as a host in this study. Strain IMCC26059 belongs to the family 28, IMCC26059 showed a close relationship with several type strains of the family Comamonadaceae: Curvibacter delicatus (97.93%), Curvibacter fontanus (97.92%), Rhodoferax saidenbachensis (97.85%), and Albidiferax ferrireducens (97.27%). A phylogenetic tree built based on 16 S rRNA gene sequences also placed IMCC26059 clearly within the family Comamonadaceae, although the taxonomic status within the family could not be resolved unambiguously , the two phages were revealed to have different morphologies as well. P26059A belonged to the family Siphoviridae with a long tail of 153 nm in length and 62 nm head in diameter  and an icosahedral shaped head (59 nm in diameter)  Fig. . One-steer) Fig. . Two phaRhodoferax, Curvibacter, Limnohabitans, and Polaromonas that were tested, none of the bacterial strains except for its original host (IMCC26059) showed infectivity, indicating that phages P26059A and P26059B had a very narrow host range.To determine the host range of the two phages, bacterial strains of phylogenetically-related genera to strain IMCC26059 were infected by phages P26059A and P26059B. Of 12 strains belonging to the genera The genomes of the two phages were sequenced using the Illumina MiSeq platform. The genome size of P26069A was revealed to be 84,008 bp with 43.6% G + C content, and that of P26059B was shown to be 41,344 bp long with 54.3% G + C content. The two phage genomes had little sequence similarity at the nucleotide level. The RAST server predicted 124 protein-coding genes within the P26059A genome, and 46 of them within the P26059B genome Table . When P2Caulobacter phage Percy. Genes coding for HNH endonucleases, which are also widely distributed bacteriophage proteins, were found in both phage genomes. ORFs 15, 56, and 58 of P26059A and ORF 11 of P26059B were all identified to belong to the HNH endonuclease 3 protein family (PF13392); however, the sequences were too divergent such that all four protein-coding genes were dissimilar from each other. As related to the DNA replication mechanism genes, two phages carried DNA polymerases of different types. P26059A carried a family B DNA polymerase (ORF 55), while P26059B carried a family A DNA polymerase (ORF 27). In addition, P26059B had a DNA-dependent RNA polymerase gene (ORF 18).Although the two genomes were not similar to each other at the nucleotide level, they shared a number of genes with similar functions. Both phages carried genes that coded for general bacteriophage proteins such as terminase, capsid, tail proteins, and endonucleases Fig. . One of Podoviridae. The P26059A TerL protein was clustered with representative TerL proteins of the family Myoviridae, although P26059A was morphologically classified as a member of the family Siphoviridae. Bacteriophage genomes are often comprised of genome fragments that show similarities to different phages and bacteria, which might have been accumulated from horizontal transfers during infections29. The genome of P26059A may also have acquired the terL gene through horizontal transfer from other phages or its host.To analyze the phylogenetic relationship between the two phages and other phages deposited in public databases, a phylogenetic tree was constructed using TerL sequences that were carried by both phages. The TerL sequence of the two phages were too divergent from each other to be classified into a monophyletic group Fig. . Phage P30, leading to the active replication of phage genomes. The phage P26059A genome also encoded for the PhoH family protein (ORF 117), which is known to be well distributed among marine bacteriophages and cyanophages, and has been used as a phylogenetic marker for phage studies31. In the phylogenetic analysis using the phoH gene, phage P26059A was distantly related with the freshwater Microcystis phage Ma-Lmm01 . The GIY-YIG domains are found in endonucleases that function as repair systems of damaged DNA in prokaryotes. Within the bacteriophage genome, it is known to function in cleaving the host DNA to utilize the host nucleotides in phage genome replication32. An integration host factor (IHF) encoded by ORF 82 may also be involved in the packaging of the phage genome into the capsid through the condensation of genomic DNA33. Phage P26059A also has genes for defense mechanism. The ORF 85 encoded for a Lar family protein, a restriction alleviation protein34, which protects the phage genome from host restriction endonucleases. ORF 108 encoded for a superinfection immunity protein to prevent infections of secondary phages upon infection of the first36. The presence of such defense mechanisms may provide advantages to P26059A over other phages infecting the identical host.P26059A also carried diverse proteases with different functions. A number of conventional proteases found in phage genomes were also found in the genome of P26059A, such as the serine protease XkdF (ORF 10), peptidase M15 (ORF 35), and cell wall hydrolase (ORF 103). ORF 116 encoded for a caseinolytic protease (ClpP) which is commonly found in bacteria. Within many bacterial cells, ClpP forms a chaperone-protease pair with the ClpA chaperone and takes part in protein degradation. ClpP has been found in bacteriophage genomes as well and it was revealed earlier that this protease acts as a prohead protease for packaging38, was also predicted between 61,298 and 61,396 bp.Within the genome of P26059A, ORF 59 was annotated as tRNA nucleotidyl transferase/poly (A) polymerase, which contributes in tRNA elongation (Supplementary Table 17. The sequences of the Lake Soyang virome were binned against a custom-made database (see Methods) using the DIAMOND algorithm39, resulting in each of the virome sequences to be assigned to the best-matching protein in the database. Among all of the assigned reads, about 12.5% (SY-′15 Jan.) to 29.4% (SY-′15 Sept.) of the sequences were assigned to viral proteins  and 13th (1.21%), respectively, which suggested that the distribution patterns of the two phage groups were distinctively different from Lake Soyang.To explore the distribution of P26059A- or P26059B-related phage groups in other freshwater habitats, 9 randomly selected virome data of Lake Michigan were collected and analyzed using the same method. Compared to the Lake Soyang virome, a lesser portion of the binned reads were assigned to viral proteins; 0.4% (SRR197488) to 2.9% (SRR1974494), while most of the reads were assigned to bacterial proteins  and Podoviridae (P26059B). The genome sequences of the two phages were also very different in length , and showed little sequence similarity, although a number of typical phage proteins, such as terminase, were predicted in both genomes. The P26059A genome carried auxiliary metabolic genes including phoH and clpP that may promote phage amplification. Albeit at low proportions, sequences similar to the two phage genomes were consistently detected in the virome data analyzed in this study. Especially P26059A, which appeared to be more abundant than P26059B, had a seasonal preference for the summer (SY-′15 Sept.). Moreover, a contig highly similar and syntenic to the P26059A genome was also found from the SY-′15 Sept. virome, implying the presence of a phage population sharing much of their genomic contents with P26059A. Prevalence of phages P26059A and P26059B in Lake Soyang indicated a consistent lytic infection of Comamonadaceae strain IMCC26059 by the two phages, which might control the population size of this bacterial group. The finding that many characteristics including morphology, genome features, and ecological abundance were different between the two phages sharing the identical host implies that physical and genomic information on phages themselves was not sufficient to infer putative hosts. Thus, along with numerous viral metagenome studies, the isolation and identification of phages infecting diverse bacterial strains must be performed for better interpretation and classification of novel phage genomes retrieved from immense virome data. This study represents one of the very few reports on freshwater phages infecting the family Comamonadaceae, an abundant and ubiquitous freshwater bacterial group. Therefore, the two phages, P26059A and P26059B, are expected to contribute to future research on freshwater phages.In this study, we have successfully isolated two novel lytic phages that infected a single host bacterial strain of the family Comamonadaceae strain named IMCC26059 was isolated from Lake Soyang, which is located in South Korea . Strain IMCC26059 was grown and maintained on R2A agar  at 20 °C and used as a host to screen for its bacteriophages. In May 2015, surface water from Lake Soyang was collected and brought to the lab at 4 °C. The water sample was filtered through a 0.2-μm PES membrane filter 7 to remove large particles and bacteria, and retain only particles smaller than 0.2-μm in diameter, which was mostly comprised of viral particles. To 400 ml of filtered water sample, 100 ml of 5 × R2A broth  and liquid culture of IMCC26059 were added to enrich probable bacteriophages infecting IMCC26059 as described previously26. From this enrichment culture, a plaque-forming lytic phage was successfully isolated and it was named as P26509A. In June 2016, another surface water sample was collected from the identical site. After filtering the water sample through a 0.2-μm PES membrane filter, 1 L of the water sample was concentrated to approximately 12 ml by ultrafiltration using a 30 kDa Centrifugal Device . The sample was filtered through a 0.2-μm Acrodisc® Syringe Filter for sterilization. Ten microliters of the sample were spotted on a IMCC26059 bacterial lawn plate and plaques were obtained from the spotted regions. The plaque was retrieved and purified through a series of double agar layer (DAL) plating and named as P26059B.In April 2014, a 8 cells/ml for IMCC26059 and 2.43 × 108 PFU/ml and 7.98 × 108 PFU/ml for P26059A and P26059B, respectively) prepared in SM buffer . The phage stocks were each inoculated to 10 ml of host liquid culture at a multiplicity of infection (MOI) of 0.05. The mixtures were incubated in a shaking incubator at 20 °C and 100 rpm for 10 min. After incubation, cells infected with phage particles were washed twice by centrifuging at 2,000 × g for 20 min at 4 °C and resuspending in 1 ml of SM buffer, to remove unabsorbed phage particles. The incubated samples were diluted by 10−4-fold and were placed in a shaking incubator for 3 hours and 40 min. During the incubation, the liquid culture was withdrawn every 20 min (10 min for P26059B) and was used for a plaque assay based on the DAL method in triplicate. The plaques were counted after overnight incubation at 20 °C, and the obtained plaque numbers were used to construct one-step growth curves. Burst sizes of the two phages were estimated by calculating the ratio of maximum titer of the phage to the infection centers (number of plaques at latent period)40.One-step growth curves for P26059A and P26059B were generated using exponentially growing IMCC26059 liquid culture and phage stocks (~1 × 10: Rhodoferax saidenbachensis (DSM 22694), R. fermentans (KACC 15304), Curvibacter fontanous (KCTC 42832), C. lanceolatus (KACC 11655), C. delicatus (KACC 12205), C. gracilis (KACC 11677), Limnohabitans planktonicus (DSM 21594), L. curvus (DSM 21645), L. parvus (DSM 21592), L. australis (DSM 21646), Polaromonas jejuensis (KCTC 12508), and P. aquatica (KCTC 11696).Host range for phages P26059A and P26059B was determined by observing plaque formation after dropping 10 μl of phage stock onto fresh bacterial lawn plates of phylogenetically-related bacterial strains to the original host, IMCC26059  8000  was added to a final concentration of 10% (w/v) and incubated in 4 °C for overnight. After incubation, the samples were centrifuged at 11,000 × g for 40 min to pellet the phage particles. The phage pellets were resuspended in 3–5 ml of SM buffer, and an equal volume of chloroform was treated to remove PEG. The samples were further concentrated by ultracentrifugation at 45,000 × g for 2 hrs (Beckman Coulter L-90K ultracentrifuge with a SW 50 Ti rotor) and the obtained phage pellets were resuspended in 100 μl of SM buffer for further procedures.For DNA extraction and TEM sample preparation, the phages were amplified and concentrated according to the ‘Molecular Cloning: A Laboratory Manual’42 and were observed under a transmission electron microscope . After observation, taxonomic classification of phages were made based on their morphology43.The phage concentrates from above were adsorbed onto formvar and carbon-coated copper grids. The grids were negatively stained using 2% uranyl acetate44. For both phages, the complete genome sequences were obtained as single circular contigs and were annotated by the RAST server45. Each predicted gene was analyzed by BLASTP against NCBI’s nr and env-nr protein databases46 for their function prediction, and only the results with e-values less than 0.001 were accepted. The protein coding genes that did not have a predicted function were further analyzed using the UniProt database47, Pfam database48, and Conserved Domain Database (CDD)49. Then their functions were predicted based on the protein domain found with an e-value threshold of 0.001. The tRNAs were searched using tRNAscan-SE v. 2.050 and ARAGORN v. 1.2.3851, which were available online.Genomic DNA was extracted from the phage concentrates using the Qiagen DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions. Whole genome sequencing for the phage genomes was performed by ChunLab, Inc. . The sequencing libraries were constructed using the TruSeq DNA sample preparation kit and were sequenced using an Illumina MiSeq system with 2 × 300-bp paired-end reads. Raw sequencing reads for P26059A and P26059B were assembled using SPAdes versions 3.5.0 and 3.8.2, respectivelyCaudovirales. The terminase sequences of the two phages were aligned with those of other reference bacteriophages within the order Caudovirales, which were collected from the NCBI nr database, using CLUSTALX52 implanted in the MEGA 653. A neighbor-joining phylogenetic tree for terminase large subunit amino acid sequences was constructed using the Kimura-2 parameter model. The robustness of the tree topology was assessed by bootstrap analyses based on 1,000 random resamplings. The phylogenetic position of the PhoH protein encoded in the P26059A genome was also inferred by the same method used for TerL phylogeny.Based on the amino acid sequences obtained from above, phylogenetic analyses for the two phages were made. To construct the phylogenetic tree, sequences for the terminase large subunit (TerL) were selected, which is the most widely found and universally used genetic marker for the order 17, the original habitat of the phages, and 9 viromes of Lake Michigan (PRJNA248239)54 following the protocol of Moon et al.17. In brief, a search database was constructed by the addition of proteins predicted in the two phages to all viral and bacterial (non-redundant) proteins of the NCBI RefSeq database (release 79). Sequencing reads of the viromes were assigned to the best-matching protein of the search database using the DIAMOND version 0.8.36 (–query-gencode 11 –single-domain)39. All of the reads that were assigned to bacteria were disregarded and only those assigned to bacteriophage and viruses were considered for analysis. To search for viral metagenomic contig syntenic to the P26059A genome, virome data from Lake Soyang (’15 Sept.) were assembled using SPAdes version 3.8.2. The similarities between the assembled contigs (≥10 kb) and the P26059A genome were estimated based on total bitscores from tBLASTx analysis, and contigs showing higher bitscores were selected. The overall synteny between the selected contigs and the P26059A genome was demonstrated using EasyFig55 based on the tBLASTn comparison.In order to analyze the distribution and abundance of phage populations related to the two phages, a competitive binning analysis was performed for 6 viromes of Lake Soyang (PRJEB15535)The genome sequences for phages P26059A and P26059B were deposited in the NCBI GenBank database with accession numbers of KY981271 and KY981272, respectively.Supplementary information


Harvesting valuable bioproducts from various renewable feedstocks is necessary for the critical development of a sustainable bioeconomy. Anaerobic digestion is a well-established technology for the conversion of wastewater and solid feedstocks to energy with the additional potential for production of process intermediates of high market values . In recent years, first-generation biofuels typically derived from food crops have been widely utilized as a renewable source of energy. The environmental and socioeconomic limitations of such strategy, however, have led to the development of second-generation biofuels utilizing, amongst other feedstocks, lignocellulosic biomass. In this context, the anaerobic digestion of perennial grass holds great promise for the conversion of sustainable renewable feedstock to energy and other process intermediates. The advancement of this technology however, and its implementation for industrial applications, relies on a greater understanding of the microbiome underpinning the process. To this end, microbial communities recovered from replicated anaerobic bioreactors digesting grass were analyzed. The bioreactors leachates were not buffered and acidic pH (between 5.5 and 6.3) prevailed at the time of sampling as a result of microbial activities. Community composition and transcriptionally active taxa were examined using 16S rRNA sequencing and microbial functions were investigated using metaproteomics. Bioreactor fraction, i.e., grass or leachate, was found to be the main discriminator of community analysis across the three molecular level of investigation . Six taxa, namely Bacteroidia, Betaproteobacteria, Clostridia, Gammaproteobacteria, Methanomicrobia, and Negativicutes accounted for the large majority of the three datasets. The initial stages of grass hydrolysis were carried out by Bacteroidia, Gammaproteobacteria, and Negativicutes in the grass biofilms, in addition to Clostridia in the bioreactor leachates. Numerous glycolytic enzymes and carbohydrate transporters were detected throughout the bioreactors in addition to proteins involved in butanol and lactate production. Finally, evidence of the prevalence of stressful conditions within the bioreactors and particularly impacting Clostridia was observed in the metaproteomes. Taken together, this study highlights the functional importance of Clostridia during the anaerobic digestion of grass and thus research avenues allowing members of this taxon to thrive should be explored. The development of the bioeconomy is critical in attaining several of the UN Sustainable Development Goals for 2030 , includiRecently, there has been a considerable increase in energy crops usage in the biogas industry  with cor2/CO2 generation and finally; (iv) methanogenesis where acetate and H2/CO2 are converted to CH4  hydrolysis where polymers are converted to monomers; (ii) acidogenesis leading to volatile fatty acid production; (iii) acetogenesis leading to acetate and Hd to CH4 . Even thd to CH4 . Reactord to CH4 . Thus thd to CH4 , and thiRecent technological developments, and specifically the advancement of high-throughput omics, have allowed for the possibility of system approaches to be explored . Particu2, 0.2 mM H3BO3, 0.1 mM ZnCl2, 0.06 mM CuCl2, 0.01 mM NaHSO4, 0.6 mM CaCl2, 0.07 mM NiCl2 and 0.1 mM SeO2) were added. The leachate was recirculated in a down-flow mode using a peristaltic pump. At the end of each bioreactor run (7 days), 126 g VS of digestate were used to inoculate the next batch to which 84 g VS of ensiled pressed grass and 1.6 L of leachate from the previous batch supplemented with 1.6 L of freshly prepared leachate (water and trace elements) were added. VS analysis was performed gravimetrically according to the standard method of Supplementary Table S1. Chemical oxygen demand (COD) measurements were performed according to the g for 15 min at 4°C, prior to resuspension in 1 ml of 10 mM Tris Base, 0.1 mM EDTA and 5 mM MgCl2 (resuspension buffer) and storage on ice before further use. Digestate samples were carefully drained, then immersed in 250 ml of resuspension buffer and placed in a sonication bath for 5 min to gently detach the grass biofilms. After grass removal, the digestate samples were filtered through two layers of muslin cloth twice before centrifugation at 8,000 × g for 15 min at 4°C. The resulting pellets were resuspended in 1 ml of resuspension buffer. Leachate and digestate samples were then centrifuged at 17,000 × g for 10 min before undergoing the following series of washes , with a working volume of 4 L, were operated at 37°C in a semi-continuous mode with a solid retention time of 7 days as previously described . For thef washes : twice w-1 β-mercaptoethanol were added. Cell lysis was carried out by bead beating for 30 s using zirconia beads (0.5 ml: 0.1 mm and 0.5 mm diameter in 1:1 ratio). The samples were then centrifuged at 17,000 × g for 30 min, and this step was repeated until no pellet was visible. The resulting supernatants were supplemented with 100 μl pure ethanol and loaded onto all-in-one chromatographic spin columns (Norgen Biotek). Purification and isolation of DNA, RNA and proteins were carried out following the manufacturer’s recommendations. DNase treatment of RNA samples was performed using the Turbo DNA-free kit (Ambion). Control PCRs using DNase treated products as templates were carried out to ensure that no DNA remained in the RNA samples prior to cDNA generation using SuperScript III Reverse Transcriptase (Invitrogen), flash freezing in liquid nitrogen and storage at -80°C. Both cDNA and DNA samples were prepared for paired-end 16S rRNA sequencing using Illumina Miseq platform and Golay barcodes. Amplification of the 16S rRNA gene from DNA and cDNA samples was carried out in triplicate 25 μl reactions using 515F/806R primers  as follows: 1X Q5® reaction buffer, 200 μM dNTPs, 0.5 μM of each primer, 0.02 U μl-1 of Q5® TAQ polymerase and 500 ng of template. PCR conditions consisted of a hotstart at 98°C for 30 s, followed by 30 cycles of denaturation at 98°C for 10 s, annealing at 52°C for 30 s and elongation at 72°C for 30 s and a final elongation step at 72°C for 2 min. The replicate amplicons were then pooled and quantified using the Qubit dsDNA HS Assay kit (Life Technologies) following the manufacturers’ instructions. Samples were normalized to 3 ng μl-1 and pooled together, prior to Illumina sequencing. A total of 24 samples were analyzed, corresponding to duplicate samples from both grass biofilms and leachate fractions from the triplicate bioreactors. Illumina sequencing was carried out by the Centre for Genomic Research  and generated a total of 2.13 107 reads corresponding to 1.14 × 107 and 9.98 × 106 DNA and cDNA sequences, respectively. Only 20 reads were obtained for one of the duplicate grass biofilm cDNA samples from R1, which was therefore dropped from further analysis. Sequencing data were analyzed using the Illumina Amplicon Processing Workflow available at: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/Illumina_workflow.html. Briefly, paired-end reads were trimmed, overlapped and assembled, prior to OTU clustering and chimera removal using the gold database from UCHIME. Phylogenetic trees and OTU assignments were carried out using MUSCLE. The DNA and cDNA sequences were deposited on NCBI’s Sequence Read Archive under the accession number SRP119456.DNA, RNA, and proteins were co-extracted from digestate and leachate samples using the RNA/DNA/Protein Purification kit from Norgen Biotek. Briefly, digestate and leachate cell pellets were resuspended in 500 μl lysis solution and 500 μl 1X Tris-EDTA to which 10 μl mlTM kit , following the manufacturer’s instructions. Protein samples were normalized to a concentration of 1.3 μg μl-1 and analyzed by GeLC MS/MS  of 2 (corresponding to protein detection with ≥99% confidence) and a minimum of two peptides were employed for protein identification. When protein assignment was ambiguous, i.e., when a protein was assigned to multiple species, the lowest common ancestor is reported. Analysis of clusters of orthologous groups (COGs) was carried out using MEGAN51 and an overview of metabolic pathways from which proteins were identified was generated using the Metaproteomics Data Analysis Workflow available at http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/Metaproteomics.html. In this pipeline, the enzyme commission (EC) numbers corresponding to the identified proteins are retrieved when available, MinPath /(Nc/N), where nc is the number of hits to a given category ‘c’  in a specific grass dataset , n is the total number of hits in all categories in the same grass dataset, Nc is the number of hits to that category in the corresponding leachate dataset and N is the number of hits in all categories in the same leachate dataset. Ratios smaller than 1 indicate an under-representation of a specific category ‘c’ among grass samples compared to leachate samples, with ratios greater than 1 corresponding to an over-representation of a specific category in the grass samples. Statistical over- and under-representation of a given taxonomic assignment between two datasets was determined by pairwise comparisons using two-tailed Fishers’ exact test with confidence intervals at 99% significance (Padj < 0.05).Non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances was performed using the statistical program R  to compaFigure 1). NMDS ordination positions each sample as a function of its distance from all other data points. An NMDS plot stress value below 0.1 indicates that the two-dimensional representation is ideal for data interpretation . The observed sample groupings were found to be statistically significant using ANOSIM, with the exception of DNA samples for which grass and leachate bioreactor fractions could not be satisfactorily distinguished . ANOSIM analysis also indicated that the samples did not cluster as a function of bioreactors . Taken together, the results suggest that molecular  and bioreactor  fractions were the main drivers of microbial community structure. Six taxa, namely Bacteroidia, Betaproteobacteria, Clostridia, Gammaproteobacteria, Methanomicrobia, and Negativicutes, in addition to sequences classified as unknown, accounted for up to 93% of OTUs in the DNA, and 98% in the cDNA, datasets . Proteins were also assigned predominantly to these six phylogenetic classes  with only a few proteins assigned to unknown species , as protein-coding sequences from unknown microorganisms are unlikely to be present in the NCBInr database. In addition, the overall contribution of these six phylogenetic classes to protein assignment was less than the contribution to DNA and cDNA. This was attributed to a large proportion of proteins that shared a lowest common ancestor at a taxonomic level higher than class . Unclassified OTUs accounted for up to 50% of the microbial community in cDNA samples, highlighting the likely contribution of yet unknown species to the AD of grass . Overall, differences in the relative abundance of the six main taxa could be observed amongst the three molecular datasets . For example, Bacteroidia’s relative abundance was higher in DNA and protein datasets compared to cDNA samples. Conversely, the reverse trend was observed for Negativicutes with an increased relative abundance amongst cDNA and protein datasets compared to DNA samples. Differences could also be seen between grass and leachate fractions at the three levels of molecular investigation . Bacteroidia and Clostridia were found to be over-represented in the leachate compared to grass biofilms in DNA, cDNA, and protein samples . Similarly, Gammaproteobacteria and Methanomicrobia were over-represented in the grass biofilm datasets across the three level of molecular information . Negativicutes were under-represented in the grass fraction in both DNA and cDNA datasets while a statistically significant increased number of proteins were assigned to this taxon in the grass fraction when compared to leachate samples . This observation is unlikely resulting from a bias in genome availability, as only 84 Negativicutes full genomes are currently available in the NCBI database against, for example, over 1880 Gammaproteobacteria genomes. Finally, Betaproteobacteria and OTUs classified as unknown displayed opposite trends in DNA and cDNA datasets where they were over-represented in the leachate and in the grass fractions, respectively . For these two microbial groups no statistically significant differential distribution between grass and leachate fractions could be identified in the protein samples.16S rRNA profiling revealed a total of 1549 operational taxonomic units (OTUs) across the 24 samples analyzed . NMDS was used to visualize microbial community dissimilarities between the samples , providing an overview of the metabolic pathways likely to be active at the time of sampling . Grass is typically composed of hemicellulose (ranging from 35 to 50%), cellulose (25 to 40%), lignin (10 to 30%), free sugars (10 to 26%), and lipids . Sample dissimilarities were assessed with NMDS and ANOSIM, which indicated a clustering of the metaproteomic datasets as a function of bioreactor fraction, i.e., grass biofilms or leachate . Classifying microbial proteins into broad functional categories  did not, however, result in any statistically significant differential distribution between the grass and leachate metaproteomes . The most abundant COG categories, collectively accounting for ∼80% of both grass and leachate proteins, were the following: translation (J), energy production and conversion (C), amino acid transport and metabolism (E), post-translational modification, protein turnover and chaperones (O) and carbohydrate transport and metabolism .A total of 1,830 proteins was detected across the 12 samples analyzed . In addition, proteins involved in uronic acid degradation, a component of grass cell wall, could also be detected in both sample types . A cellulose hydrolase (beta-glucosidase) assigned to Bacteroidetes and a xylan hydrolase  assigned to Clostridium sp. could only be detected in the leachate samples (Supplementary Table S3). It is worth noting, however, that the non-detection of a protein does not necessarily imply that it was not expressed at the time of sampling. A galactose hydrolysing enzyme , exclusively assigned to E. coli and a Megasphaera elsdenii protein involved in glycolate metabolism were detected in both bioreactor fractions (Supplementary Table S3). Xylose degradation was attributed to Bacteroidia in grass biofilms, in addition to Clostridia in the leachate samples . Numerous proteins with functions in carbohydrate transport were also detected in all the samples analyzed and mainly assigned to Clostridia and Spirochaetales. Glycolysis was found to take place in both grass and leachate fractions as indicated by a plethora of glycolytic enzymes predominantly assigned to Bacteroidia, Clostridia and Gammaproteobacteria .Evidence of cellulose  and hemicellulose  biodegradation was reflected in both grass and leachate metaproteomes . Oxidoreductases were mainly assigned to Megasphaera elsdenii, Prevotella, and Clostridiales in all the samples analyzed . Numerous ATPases (ABC-type sugar transporter and F-type) and ATP synthases were detected in the triplicate bioreactors and assigned to Gammaproteobacteria, Negativicutes, Bacteroidia and Betaproteobacteria in all samples in addition to Clostridia specifically in the leachate fractions . Proteins with functions in the TCA cycle were expressed at the time of sampling and assigned to Gammaproteobacteria in the grass biofilms in addition to Bacteroidia in the leachate samples . Enzymes involved in the degradation of propionate to pyruvate and in the conversion of pyruvate to acetyl-coA/acetate were detected in both bioreactor fractions. Acetate could then partly be used as a substrate for methanogenesis as suggested by the detection of enzymes from the corresponding metabolic pathway and assigned to Methanosarcina in both leachate samples and grass biofilms . Lactaldehyde dehydrogenase, involved in the production of lactate, was assigned to Dysgonomonas gadei and detected in the leachate only (Supplementary Table S3). Of particular note, proteins involved in the production and conversion of energy that were assigned to Clostridia were only detected in the leachate samples suggesting a possibly more important role for this microbial group in that bioreactor fraction compared to grass biofilms .Many electron transfer proteins were found to be expressed at the time of sampling and were assigned to Figure 6 and Supplementary Table S3). Butanol production was exclusively attributed to Megasphaera elsdenii in the grass biofilms, in addition to Clostridia in leachate samples. Propionate degradation occurred in the leachate as evidenced by the detection of proteins involved in this metabolic pathway, namely methylmalonyl-CoA mutases assigned to Clostridia and propionyl-CoA carboxylases assigned to Bacteroidia . Proteins involved in lipid metabolism that were assigned to Clostridia were only detected in the leachate samples. Proteins assigned to this taxon with roles in amino acid metabolism were detected in both grass and leachate bioreactor fractions, where Clostridia were found to be involved in the fermentation of glutamate to butyrate . Bacteroidia and Negativicutes were also implicated in this process with the identification of 2-hydroxyglutaryl-CoA dehydratase and numerous glutamate dehydrogenases (Supplementary Table S3). Differences in the taxonomic assignment of proteins involved in amino acid transport and metabolism could be seen between the grass and leachate samples, with for example 17 and 12% of proteins assigned to Negativicutes and Gammaproteobacteria in the grass biofilms compared to 2% and none assigned to these taxa in the leachate fraction (see interactive view of the Krona plots).Fatty acid β oxidation and pyruvate fermentation to butanol were found to take place in both leachate and grass bioreactor fractions . Chaperones are essential for protein folding but also for the re-folding of stress-denatured proteins. Numerous GroEL (60 kDa) and GroES (10 kDa) chaperonins were detected and assigned, amongst other taxa, to Clostridia and Bacteroidia (Supplementary Table S3). Recently, the co-expression of GroEL-GroES was found to be imperative for the production of a functional xylose isomerase in Saccharomyces cerevisiae (Supplementary Table S3). Trigger factor proteins, ClpB, DnaK, and heat shock protein 90 (Hsp90) were also found to be expressed in the bioreactors’ grass biofilms and leachate samples (Supplementary Table S3). Trigger factors protect protein nascent chains from aggregation, and play important roles in the stabilization of partially folded proteins (Supplementary Table S3). Desulfoferrodoxin, rubrerythrin, rubredoxin, and superoxide dismutase were detected in both bioreactor fractions in addition to alkyl hydroperoxide reductase assigned to Proteobacteria and glutathione peroxidase from Megasphaera elsdenii which were only detected in the leachate samples (Supplementary Table S3). Desulfoferrodoxin and superoxide dismutase catalyze the conversion of superoxide radicals to hydrogen peroxide, which is then reduced to water by rubrerythrin and rubredoxin with the latter involved in electron transfer during the oxidation process . Specifically, proteins involved in stage V of sporulation were detected in the two bioreactors’ fractions. This sporulation stage is one of the latest of the process and corresponds to the spore outer coat deposition (Supplementary Table S3). This observation might suggest that phosphate is limiting in the leachate bioreactor fraction. Interestingly, an aminoacyl-histidine dipeptidase (PepD) was detected in one of the bioreactor leachate samples (Supplementary Table S3) and pepD has been shown to be up-regulated during phosphate starvation . The same trends were reflected in the DNA and cDNA datasets . Taken together these results might indicate a possible preference for biofilm lifestyle as opposed to planktonic for Gammaproteobacteria and Methanomicrobia under the conditions experienced within the bioreactors. Focusing on the anaerobic process of grass acidification, using a similar experimental strategy as the one employed here, whereby acidification resulted from microbial activities, Clostridium . Additionally, a lactaldehyde dehydrogenase from Dysgonomonas gadei (Bacteroidia), indicative of lactate production, was detected in the leachate bioreactor fractions (Supplementary Table S3). Lactate fermentation was also reported in biogas plants with the detection of E. coli and Lactobacillales enzymes involved in this metabolic pathway (Supplementary Table S3). Clostridia, accounting for 14 and 20% of the grass and leachate metaproteomes, were involved in the initial stages of grass hydrolysis only in the leachate samples, while Bacteroidia, Gammaproteobacteria, and Negativicutes were implicated in this process in the grass biofilms . Overall a very similar distribution of functional activities was observed in grass biofilms and leachate samples . This, combined with differences in phylogenetic assignment distribution is indicative of functional redundancy whereby the same microbial functions are taking place throughout the replicated bioreactors but are driven by different microbial taxa. It is worth noting that sampling earlier in the bioreactor run might have led to a different conclusion. Evidence of environmental stress conditions prevailing within the bioreactors could be obtained in the metaproteomes. Clostridia seemed particularly affected as suggested by the expression of multiple chaperones, as well as proteins involved in oxidative stress response and sporulation . Even though several microbial groups of Methanomicrobia were identified in the DNA and cDNA datasets, including Methanobacterium, Methanobrevibacter, and Methanosaeta, only proteins from Methanosarcineae were detected in the bioreactors (Supplementary Table S1). This observation, together with the detection of stress response proteins, might point to the prevalence of inhospitable environmental conditions at the time of sampling. Indeed, Methanosarcineae have been shown to thrive under sub-optimal anaerobic bioreactor operating conditions . Similar microbial groups and functions were detected across the two bioreactor fractions with varying abundance for the 16S rRNA datasets and with changes in phylogenetic assignments for the metaproteomes. Six main taxonomic classes, together with OTUs classified as unknown accounted for the large majority of the three datasets. An overview of the main microbial functions, occurring in the bioreactors at the time of sampling, together with their corresponding phylogenetic assignments is presented in stridium . Here, b pathway . Lactate pathway  but no enditions . OverallSRP119456. Metaproteomic data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository (dataset identifier PXD007956). An interactive view of the Krona plots from Figure 6 can be accessed here: https://htmlpreview.github.io/?https://github.com/FlorenceAbram/Grass-AD-study/blob/master/Krona_Metaproteomics.html.16SrRNA sequence data were deposited on NCBI’s Sequence Read Archive under the accession number FA designed the research. AV ran the bioreactors. AJ carried out the biomolecule co-extraction and prepared all samples for downstreams analyses. SS and CB performed the MS analysis. UI, AJ, FA, and CQ carried out the bioinformatic analyses. FA, AJ, and CN analyzed the data. FA and AJ wrote the paper with input from VO.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chelonia mydas) are one of the three groups of marine tetrapods with an herbivorous diet and which acquire it after several years consuming pelagic animals. We characterized the microbiota present in the feces and rectum of 24 young wild and captive green turtles from the coastal waters of Brazil, with curved carapace length ranging from 31.1 to 64.7 cm, to test the hypotheses that (1) the ontogenetic dietary shift after settlement is followed by a gradual change in the composition and diversity of the gut microbiome, (2) differences exist between the composition and diversity of the gut microbiome of green turtles from tropical and subtropical regions, and (3) the consumption of omnivorous diets modifies the gut microbiota of green turtles.Tetrapods do not express hydrolases for cellulose and hemicellulose assimilation, and hence, the independent acquisition of herbivory required the establishment of new endosymbiotic relationships between tetrapods and microbes. Green turtles . The results indicated that most of the juvenile green turtles less than 45 cm of curved carapace length exhibited a fecal microbiota co-dominated by representatives of the phyla These results indicate that juvenile green turtles from the coastal waters of Brazil had the same general microbiota, regardless of body size and origin, and suggest a fast acquisition of a polysaccharide fermenting gut microbiota by juvenile green turtles after settlement into coastal habitats.The online version of this article (10.1186/s40168-018-0454-z) contains supplementary material, which is available to authorized users. Herbivory has evolved independently in several groups of tetrapods belonging to diverse evolutionary lineages . Unlike Amblyrhynchus cristatus), and the green turtle (Chelonia mydas). Sirenian diet is dominated by seagrasses [Several groups of tetrapods have recolonised the marine environment after independent evolution in land, but only three of them are herbivores: sirenians (manatees and the dugong), the marine iguana  were collected from subtropical Ubatuba , in the northern coast of the state of Sao Paulo. Rocky reefs and sandy beaches dominate the coastline of Ubatuba [n = 5) were collected from tropical Praia do Forte (12° 38′ S 38° 05′ W), located 70 km from Salvador do Bahia. The coastline is characterized by the presence of shallow coral reefs with substantial air exposition during low tide [Two different areas of Brazil were sampled in February to March 2016. Most samples  used by local fishermen and consisting on fixed nets attached to the seafloor [www.tamar.org.br), as part of the long-term study on the abundance and habitat use of green turtles along the Brazilian coast. After capture, curved carapace length (CCL) was measured with a flexible tape  and turtles were moved to the facilities of Projeto Tamar in Ubatuba. These turtles were confined in individual PVC tanks until the moment they defecated, between 24 and 36 h after capture, and then released back to the sea at the same place of capture. Tanks had been previously disinfected with regular bleach. The core of each fecal pellet was accessed using sterilized forceps and sampled with a swab, to reduce as much as possible contamination from water. Additionally, rectal samples (n = 5) were collected with a swab during the necropsy of recently dead turtles at Praia do Forte.Fecal samples were collected from 8 turtles held in captive at the facilities of Projeto Tamar at Ubatuba and 11 wild turtles from Ubatuba. Some wild green turtles were captured alive in weirs . These gene libraries were constructed by targeting the V1–V3 hypervariable regions with the primer set 27F (5′-AGRGTTTGATCMTGGCTCAG-3′)/519R (5′-GTNTTACNGCGGCKGCTG-3′) as previously described in [DNA was extracted from a subsample of 0.25 g from each fecal or rectal sample using the PowerSoil DNA kit  following the manufacturer’s instructions. All DNA extracts were kept frozen at − 20 °C until further analysis. Massive bar-coded 16S rRNA gene-based libraries in the ribed in . The obtribed in . Qualityribed in , pickingribed in  and, forribed in  and Chimribed in . OTUs weribed in .A general lineal model (GLM) using locality (Ubatuba vs. Praia do Forte) as a fixed factor and turtle curved carapace length as a covariable was used to test the hypothesis that the microbial diversity of wild green turtles increases with turtle size and varies across localities. A general lineal model using origin (captive vs. wild) as a fixed factor and turtles curved carapace length as a covariable was used to test the hypothesis that the microbial diversity of green turtles increases with turtle size and differs between captive and wild green turtles from subtropical Ubatuba. GLMs were run in IBM SPSS Statistics 23. Multivariate principal coordinate analysis (PCoA) based on Bray-Curtis similarity distances was carried out on the OTUs incidence matrix using the CANOCO software package, version 5 , to identify clusters of green turtles differing in the community structure of their microbiomes.2,18 = 1.750, p = 0.205; Chao1: F2,18 = 1.922, p = 0.179; Shannon: F2,18 = 2.445, p = 0.118).The gut microbiome of 24 green turtles ranging in curved carapace length (CCL) from 31.1 to 64.7 cm was studied. A genomic library of 2,187,066 valid eubacterial 16S rRNA reads was obtained from their feces , and Firmicutes with a 24–56% of RA  was characterized by a high RA of representatives from the phyla Proteobacteria (approximately 60% RA) and Actinobacteria, which in this latter phylum belonged to the Mycobacterium genus . The main OTUs of the former Proteobacteria phylum were related to Burkholderia spp. (Betaproteobacteria), Sphingopyxis spp. (Alphaproteobacteria), and Pseudomonas spp. (Gammaproteobacteria), which combined represented 49.5 and 38.3% RA for UB7 and UB10, respectively. Except for Sphingopyxis, these genera have been associated to the presence of Staphylococcus spp., in the phylum Firmicutes . Regarding the bacterial community of the anomalous captive turtle (UB18), it was characterized by a high abundance of the phylum Fusobacteria (27% RA). Such dominance was primarily caused by OTU7, affiliated with the microaerotolerant fermentative Cetobacterium sp. (96% of similarity to Cetobacterium ceti), also found in whale, dolphin, and porpoise gut flora .The dominant phyla in the majority of wild and captive turtles were  RA Fig. . In mostProteobacteria was consistently higher in captive (range 0.7–7.7% RA) than in wild (range 0.2–1.9% RA) turtles from Ubatuba . On the other hand, Akkermansia spp., belonging to the phylum Verrumicrobia, was found with a RA of 8–15% in captive turtles UB18, UB20, and UB21. It is noteworthy that in one of the wild individuals (UB14), Akkermansia was enriched up to a 30% of RA and, curiously, the microbiome of this individual was rather different from that of other wild turtles.When those three anomalous turtles  were removed from the analysis, the abundance of Clostridiaceae comprised a ribotype (OTU1) that was predominant in almost all samples (from 1 to 8% RA). OTU1 belongs to the unclassified Clostridiaceae 1 subfamily. Interestingly, the RA of OTU1 in the wild turtles with the most dissimilar microbiome (UB7 and UB10) was < 0.1% RA  to Treponema brennaborense and might therefore correspond to an undescribed species. Furthermore, samples from Praia do Forte had a lower abundance of representatives in the Actinobacteria and Verrumicrobia, when compared to the Ubatuba individuals.The family otype OTU that wasRA Figs. . MoreoveAkkermansia spp., while a second larger one also contained the samples from Praia do Forte forming a very compact subcluster.Multivariate analysis (PCoA of samples’ Bray-Curtis distances based on OTUs incidence) Fig.  showed tBacteroidetes and Firmicutes and high levels of Clostridiaceae, Porphyromonas, Ruminococcaceae, and Lachnospiraceae within the latter phylum. Furthermore, this was the only microbiota profile found in wild green turtles > 45 cm CCL and in most of the captive green turtles of any size feeding on a macroalgae/fish mixed diet. These results suggest a fast acquisition of a polysaccharide fermenting gut microbiota by juvenile green turtles after settlement into coastal habitats.Green turtles settle in the coastal habitats of the south-western South Atlantic when they are 30–45 cm in CCL , 41, 51.Proteobacteria had been previously reported from the cloaca of pelagic (range 17.1–21.7 cm CCL) and recently settled (29.4–34.6 cm CCL) juvenile green turtles from Florida and from the gut of omnivorous marine fishes, but not from other groups of herbivorous vertebrates , but this is probably because they were immunodepressed and not because of recent settlement. We hypothesize that the prevalence of the Proteobacteria phylum in those three individuals was because of lesions from anthropogenic impacts [Mycobacterium, from the Actinobacteria phylum, a genus very uncommon in turtles but which includes several well-known pathogens for reptiles and amphibians [Akkermansia genus (Verrumicrobiaceae family). Akkermansia is a mucin-degrading bacterium commonly found in the human gut and recently isolated in reptiles [Akkermansia induces gut inflammation and is associated with colonic diseases in mammals, but nothing is known about its pathogenicity in reptiles. It is also worth noting a small captive turtle (34.9 cm CCL) with a microbiota dominated by Bacteroidetes and Firmicutes but with a high relative abundance of Fusobacteria, a group occurring sporadically in carnivorous marine mammals [A high abundance of  impacts . The samphibians , 54. Furreptiles , 56. SevFirmicutes are characteristic of the gut and fecal microbiota of herbivorous vertebrates  and gopher tortoises (Gopherus polyphemus) , thus suggesting that seasonal differences in temperature are unlikely to not induce major changes in the relative abundance of Bacteoidetes and Firmicutes, although samples were collected in summer in both areas. A high abundance of Bacteroidetes is neither characteristic of the gut microbiota of herbivorous chelonians, as they represent only 4% of the relative abundance of bacteria in the microbiota of Galapagos giant tortoises (Geochelone nigra) [Bacteroidetes) to thrive and dominate the microbial community structure [Caretta caretta [Firmicutes, Proteobacteria, and Bacteroidetes were the most predominant microbial population in turtle feces.Another major difference between the rectal and fecal microbiota of green turtles and those of other herbivorous vertebrates is the high abundance of s) Table . Bacterohydrates , and Yuae nigra) . It is structure . Moreove caretta  found thSpirochaetes is another group of non-cellulolytic bacteria associate with specific plant substrates during digestion [Spirochaetes members exhibit enormous diversity in a free-living or host-associated life, being pathogenic or non-pathogenic, and aerobic or anaerobic [Spirochaetes, was detected in all the samples, but only in the rectal samples of three individuals from Praia do Forte  did it represented more than 2% of the relative abundance.igestion , facilitigestion . Within naerobic . This phBacteroidetes and Firmicutes were the dominant bacteria in the feces and the rectal samples of most juvenile green turtles less than 45 cm CCL, including four specimens ranging 31.1–35.0 cm CCL, indicates that they acquired a microbiota adapted to digest polysaccharides shortly after settlement. How this specialized bacterial flora is acquired by settlers remains unknown, but land and marine iguanas have been observed consuming conspecific excrements [The fact that crements , 75, whicrements , which mcrements .Bacteroidetes and Firmicutes in different organisms, including the grazer surgeonfish. Hence, it is well possible that the seaweed-based diet of turtles could similarly affect their gut microbiota by gene acquisition, considering that CAZymes and sulfatases are required for efficient seaweed degradation [Algae and seaweeds are typically rich in sulfated polysaccharides that are absent in terrestrial plants. Hence, microbiota from the phylosphere of seaweeds are characterized by high copy numbers of sulfatases in their genomes . A recenradation . This toRuminococcaceae and Lachnospiraceae. Consumption of animal material results into a slight and statistically significant increase in the relative abundance of Proteobacteria, as revealed by the differences between captive and wild healthy turtles, but the abundance of Ruminococcaceae and Lachnospiraceae remains high anyway. This suggests that omnivore is unlikely to reduce the capacity of green turtles to digest plant material.In any case, the fast acquisition after settlement in coastal areas of a microbiota adapted to ferment polysaccharides should enable green turtles to adopt an herbivorous diet soon after recruitment. This is the pattern reported from tropical areas , 83, butRuminococcaceae and Lachnospiraceae, which may explain the progressive dietary shift as they grow.Digestibility of plant material in green turtles increases with temperature  and the Ruminococcaceae and Lachnospiraceae and, hence, does not impair the capacity to ferment structural polysaccharides. However, subtropical specimens displayed a larger variability in the gut microbial community structure, which in the most extreme cases was clearly related to poor physical condition. In summary, there is no reason for a delayed ontogenetic dietary shift after settlement, unless low winter temperature reduces their capacity to digest plant material.This study revealed that juvenile green turtles from the coastal waters of Brazil had the same general microbiota profile, regardless of size and origin . This indicates a fast acquisition of a microbiota with capacity to ferment structural polysaccharides soon after settlement in the coastal waters of Brazil and that the regular consumption of animal prey does not significantly reduce the presence of Additional file 1:Phylogeny and incidence of gut eubacteria. (XLSX 1339 kb)


The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables , the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient , a global trend , a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle  farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented  were more consistent with the objective of developing the properties targeted . Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations. The biophysical, economic and social context of agricultural production is increasingly unpredictable and volatile , driven The vulnerability of any system  is considered a function of exposure and sensitivity of that system to a range of hazards and the adaptive capacity of the system to cope with, adapt to, or recover from the effects of these conditions . More prex ante and ex post analysis, respectively.The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis . Farmersex ante simulations can be run without considering farmers’ adaptations. Still, model-based studies have several drawbacks, including (i) limited ability to reproduce farm management and production when applied to new sites (Model-based studies are interesting in that they enable strict application of the vulnerability framework by isolating farm sensitivity from adaptive capacity: ew sites  and (ii)Field-based studies have the potential to address farm adaptation and consequences of farm vulnerability in the multifactorial context farmers encounter . HoweverField-based studies are seldom reported, and examples of them have several drawbacks. Many studies are limited to a single driver of contextual change, mainly climate . Farm vuThere is a need to develop an integrated method that enables the assessment of farm vulnerability to multiple and interacting contextual change drivers (both climatic and economic) and to explain how this vulnerability can be reduced according to farm configurations and farmers’ technical adaptations over time. This is precisely the purpose of this study. To this end, we assume that considering not only the mean level but also the trend and variability of farm performances  is required to assess farm vulnerability. We have developed such a generic  method and applied it to cattle farms. Cattle farms are interesting to illustrate the merits of longitudinal analysis, since animal health and production in a given year can be strongly influenced by fodder and grain production of the previous year. Results of this analysis are presented and discussed, along with the relevance and applicability of the method.Figure 1) has two main objectives: (i) assessing farm vulnerability to the drivers of multiple contextual changes and (ii) explaining this vulnerability as a function of initial farm configurations and farmers’ multiple technical adaptations. To assess farm vulnerability to climatic and economic variability, a naive approach would consist of calculating the empirical variance of vulnerability variables for a farm over a period of time, i.e., the mean of the deviations of these vulnerability variables from their empirical mean. In doing so, however, the longitudinal aspect and the common trend would be lost, whereas farm vulnerability to climatic and economic variability can vary over time according to farmers’ adaptations. A farm with stable or increasing vulnerability variables over the study period maintains or reduces its vulnerability over time. The idea is thus to consider a trend for each vulnerability variable per farm and the deviations from this trend.Our method . These three mathematical parameters describe the mean level, trend, and variability of each vulnerability variable, respectively. Among all possible combinations, the lowest farm vulnerability to climatic and economic variability is obtained through a combination of high values of measurements , a stable or increasing trend , and low variability  for all vulnerability variables considered. In contrast, the highest farm vulnerability results from a combination of low measurements , a decreasing trend  and high variability  for all vulnerability variables considered. Our method then relates multiple farm vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability and initial farm configurations and farmers’ technical adaptations over time.Following this idea, our method considers farm vulnerability as a function of the raw measurements  of vulnerability variables, the slope of the linear regression of these measurements over time, and the residuals of this linear regression (Step 1: Choosing vulnerability and explanatory variables.Table 1A) that characterize cattle farm vulnerability and a set of explanatory variables (Table 1A) that characterize cattle farm exposure to climatic and economic variability, initial farm configurations, and farmers’ technical adaptations over time to climatic and economic variability. If needed, variables can be transformed.Variable choice involves selecting a set of vulnerability variables , overall trend , a random deviation from the overall mean for each farm, and a random deviation from the overall trend for each farm. Residuals of the random regression provide additional measures of stability. Numerically, the effects of farms as deviations from the overall mean and overall slope decrease to 0 when considered as random effects rather than fixed effects. Therefore, extreme values become less extreme in a random-regression model, which avoids over-emphasizing outliers. Analysis is performed using the statistical package lme4  in R sofThe random regression is written as:yi,j,t is the measure of the vulnerability variable for farm i in year t for level j of a certain combination of cofactors ,where α is the mean level of the vulnerability variable at time 0 (beginning of the study period),β is the mean trend of the vulnerability variable over the period,j is the mean level of the vulnerability variable at time 0 specific to a combination j of cofactors,αj is the mean trend of the vulnerability variable over the period specific to a combination j of cofactors,βai is the random effect of farm i on the level of the vulnerability variable at time 0; it is seen as a deviation from the overall mean α + αj for farm i. All ai are assumed to be independent and identically distributed (iid) bi is the random slope for farm i, the deviation from the overall trend β + βj. All bi are assumed to be iid i,t is the residual, assumed to be iid ai and bi.eab between the two random effects relative to the farm is assumed: A correlation ρyi,t for i = 1,…I and t = 1,…T is partitioned into the prediction  ei,t.The information contained in i is characterized by three aspects corresponding to (i) its level ai at the beginning of the period expressed, i.e., the intercept, as a deviation from the overall performance level of all farms; (ii) its trend bi over the period, as a deviation from the overall trend; and (iii) the variability t + αj + βjt + ai + bit.As a direct consequence of model (1), each farm i, the fitted value at time 0 (intercept), the fitted value at a given time , or the mean level of vulnerability variable, raw values, or fitted values over the entire period (the last one representing de-noised data).We used this model to study detailed measures for each farm at each point in time. The level was estimated either by aStep 3: Characterizing the diversity of farm vulnerability patterns.Descriptive analysis of farm vulnerability consists of principal component analysis (PCA) of all vulnerability variables . It identifies relationships among vulnerability variables and highlights their similarities and differences through correlations. It also distinguishes patterns of farms’ vulnerability trajectories. This makes it possible to verify whether some individuals display high  or low vulnerability according to our hypotheses . PCA is performed using the statistical package mixOmics  in R sofStep 4: Understanding farm vulnerability from explanatory variables.Tools commonly used to select linear models sometimes have difficulties choosing a subset of explanatory variables. In the context of prediction, or when explanatory variables are numerous and highly correlated, it is often useful to use statistical tools to reduce the number of dimensions. Regression on principal components  can drastically reduce the number of explanatory variables in the model. However, principal components are not necessarily related to the response variable. This was the aim of developing the partial least squares (PLS) regression: to reduce dimensionality, use orthogonal explanatory variables, and search for the maximum covariance between response and explanatory variables.Partial least squares is a multivariate projection-based method offering a compromise between PCA and linear modeling . Unlike Figure 3). At this regional scale, exposure to economic hazards is considered equal among cattle farm types  but variable over time . In contrast, exposure to climatic hazards varies both among farms and over time . Aveyron contains a range of climatic conditions, from dry lowland plains to cold and snowy mountains. As a result, it has a wide diversity of cattle farms, from highly productive maize-based systems in lowland plains to less-productive grass-based systems in the mountains. We hypothesized that farm diversity was related to the diversity of initial farm configurations and farmers’ technical adaptations to climatic and economic variability.Our study was performed in the French department of Aveyron . In the following text, farms are denoted by a letter  followed by a number corresponding to the farm. We supplemented these surveys with downscaled weather data (8 km × 8 km grid), which were needed to calculate climatic indicators and two price indices (Table 1B).The Chamber of Agriculture in Aveyron created a network of 24 beef and dairy cattle farms that are surveyed every year. Data are collected on the key aspects of livestock systems: geographic location, land use, crop and grassland yields, herd structure, animal production, animal feeding, labor requirements, and economic returns and costs. Surveys covered the period 2008–2013 and lasted 3–6 years, depending on the farm. We restricted analysis to 19 farms involved in at least 4 years of the survey (Table 1A). The two vulnerability variables measured were:The choice of vulnerability and explanatory variables relied on conceptual and indicator frameworks on adaptiveness of agricultural and socio-ecological systems . Followi1.Farm productivity based on feed internal resources, i.e., the amount of protein in milk and meat produced per year and per hectare on the farm allowed by the amount of animal feed produced on the farm over the entire animal feed consumption (on a dry matter basis) to consider farm self-sufficient productivity:Farm productivity = production of protein in milk and meat (kg protein per year) × self-sufficiency for animal feeding/usable agricultural area (ha) and self-sufficiency for animal feeding = concentrate and forage produced on-farm (kg DM per year)/total concentrate and forage consumed by the herd (kg DM per year).2.Economic efficiency of production, i.e., net economic output produced per unit of gross output:Economic efficiency of production = /gross productThe two vulnerability variables were broken down into three aspects: raw measurements, slope of the linear regression of these measurements over time, and residuals of the regression.Table 1B). Exposure to climatic variability was assessed with the following six variables:We chose eight explanatory variables to illustrate cattle-farm exposure to climatic and economic variability . All of the conceptual and indicator frameworks cited above suggest that greater diversity [i.e., variety, balance, and disparity, as defined by We selected explanatory variables to illustrate farm configurations and farmers’ technical adaptations over time . ANOVAs showed that overall levels and trends of farm productivity differed significantly among farm types . Dairy systems tended to have mean farm productivity levels (167.7 kg protein/ha/year) similar to those of mixed systems (169.3 kg protein/ha/year) and higher than those of beef systems (83.3 kg protein/ha/year). An improvement was observed in farm productivity in dairy (0.9 kg protein/ha/year) and mixed systems (3.4 kg protein/ha/year), whereas the trend declined in beef systems (-2.1 kg protein/ha/year) over the study period. However, trends differed significantly among farms, independent of farm type (P < 0.001). For instance, while D8 displayed a major improvement over the study period (6.8 kg protein/ha/year), D6 experienced a strong decline (-6.1 kg protein/ha/year). Regarding variability in farm productivity, among farm types, mixed systems (7.6 kg protein/ha/year) and beef systems (7.5 kg protein/ha/year) had lower mean absolute residuals than dairy systems (12.2 kg protein/ha/year), but these differences were not significant (P = 0.917). Independent of farm type, mean absolute residuals varied threefold, ranging from 5.2 kg protein/ha/year for D3 to 16 kg protein/ha/year for D7, but differences were also not significant (P = 1).Farm productivity varied greatly among cattle farms, with means ranging from 43 kg protein/ha/year in B2, the least productive system, to 270 kg protein/ha/year in D8, the most productive system (P < 0.001), with means ranging from 29.4%/year in B1, the least efficient system, to 66.0%/year in D6, the most efficient system . Differences were also significant among farm types (P = 0.002): dairy systems were the most efficient systems overall (56.8%), followed by mixed systems (51.5%/year) and beef systems (46.6%/year). Trends in economic efficiency also differed significantly among farm types (P < 0.012). A slight improvement was observed in beef systems (0.7%/year), compared to decreases in mixed systems (-0.6%/year) and dairy systems (-0.4%/year) over the study period. Likewise, significant differences were observed among farms, with trends (P < 0.001) ranging from -2.4%/year in D7 to 1.4%/year in D10 for dairy systems and from -2.3%/year in B3 to 6.6%/year in B7 for beef systems. When focusing on the variability in economic efficiency, differences among farm types were not significant (P = 0.901). Nonetheless, mean absolute residuals were nearly twice as high in beef systems (6.1%/year) as in dairy systems (3.3%/year) and were limited to 2.0%/year in mixed systems. Independent of farm type, between-farm differences were not significant (P = 1). Mean absolute residuals ranged from 0.9%/year in M3 to 14.7%/year in B6.Economic efficiency of production varied greatly and differed significantly among farms independent of farm type (P = 0.002) but not on the slope. Year had a significant effect overall (P = 0.04). For economic efficiency, neither farm type nor year was significant. Therefore, the final models were as follows:A random regression coefficient was fitted to each vulnerability variable, with an average effect of farm type , a per-farm average random effect, an overall time effect of farm type, and a per-farm random effect of time. A variety of hypotheses were tested within the model. For farm productivity, farm type had a significant effect on the intercept . The PCA showed that no cattle farm displayed low vulnerability to climatic and economic variability, i.e., a high level, a stable or increasing trend, and low variability for both farm productivity and economic efficiency. On the first component of the PCA (explaining 26% of the variance), the measure of farm productivity was positively correlated with the measure of economic efficiency . Among farms, this indicated that the most productive farms  could also be the most economically efficient . It was also observed that the higher the level of farm productivity, the higher its slope over the study period. All the variables mentioned above were negatively correlated with the slope of economic efficiency. Farms with the highest levels of farm productivity and economic efficiency tended to have decreasing trends of economic efficiency. The farms that experienced improvement in farm productivity (positive slopes) tended to decline in economic efficiency. The second and third components (explaining 40% of the variance) were driven mostly by residuals of the linear regression of economic efficiency and farm productivity, respectively. Hence, the second PCA axis represented a gradient of interannual variability in economic efficiency, while the third axis described a gradient of interannual variability in farm productivity.According to the Kaiser criterion, projecting the set of vulnerability variables into the PCA required analyzing three components, which explained 66% of the observed variance  in the PCA revealed contrasting patterns among farms. For instance, farms B2, B1, and B6 had low levels of farm productivity and economic efficiency, decreasing trends in farm productivity, improvement in economic efficiency, and high interannual variability. These farms were considered the most vulnerable in the sample. Conversely, farms D8, D7, and M2 were considered the least vulnerable in the sample. Analysis of all six vulnerability variables for individual farms indicated that, contrary to our hypothesis, no farm displayed a high level, a stable or increasing trend, and low residuals for both farm productivity and economic efficiency. For this reason, the least vulnerable farms represented a compromise between level, trend and variability of both performances.Q2 criterion indicated that only the first component of the PLS could be considered. Based on graphical outputs of the analysis, we also included the second component. Explanatory variables were able to explain only one vulnerability variable in the first component, the level of farm productivity, and one in the second component, the slope of farm productivity. Vulnerability variables based on economic efficiency of production were not related to any explanatory variable.The Figure 8), the level of farm productivity was positively correlated with seven management-practice variables: percentage of cropland in the farmland, percentage of silage in animal diets, Shannon index of the farmland, amount of fodder and concentrate distributed per animal, stocking rate, and mineral nitrogen fertilization rate. It was negatively correlated with two management-practice variables, namely percentage of semi-natural grasslands in the farmland and spread of calving, and two exposure variables, namely excess water in autumn and winter. Thus, it appears that farms relying solely on semi-natural grasslands with grouped calving had lower farm productivity levels than diversified systems having higher stocking and mineral fertilization rates, higher amounts of feed distributed per animal (and consequently less grazing), and calving distributed throughout the year.In the first component of the PLS regression  was mainly determined by farms’ initial positions. This indicates that adaptations implemented during the survey period did not compensate for initial differences in farm configuration and that no farm operated a substantial technical transition.Analysis of the distribution of individual data points (combinations farm × year) in the PLS identified the most and least vulnerable farms and their position with respect to climatic and economic conditions, initial farm configurations, and farmers’ technical adaptations. Distribution of farms within the factorial design of the X block of the PLS  displayed poorly diversified farmlands compared to the least vulnerable farms   that had a higher percentage of their area as cropland (31.9–42.4% vs. 3.4–18.7%). Farmland diversity distributes biomass production periods of fields over the year and reduces farm exposition to climatic hazards. The most vulnerable farms also tended to harvest less fodder in the form of silage  resulting in lower amounts of fodder distribution . As compared to hay-making, silage harvesting provides flexibility against adverse weather conditions and therefore enables to adapt to the conditions of each year.Figure 8). Different combinations of farm initial farm configurations and technical adaptations led to similar levels of vulnerability. Among the most vulnerable farms , B1 had the most diversified land use. B1 had 13%  of its area in permanent pastures vs. 67% (±12%) in grass-based ley pastures and 19% (±5%) in crops. Stocking rate was low (1.01 LU/ha) yet mineral fertilizers were applied by the farmer (29 kg N/ha). More than half (54%) of animal feed was distributed, but this percentage varied (±14%) with climatic variability. Silage represented 31% of distributed feed ±8% according to climatic conditions of each year. Concentrate distribution was stable and limited to 0.61 t DM/LU but this amount was high when considering farm productivity. Farmer B1 developed self-adaptability of his farm configuration through farmland diversity but suffered from limited technical efficiency as illustrated by the inconsistency between stocking rate, mineral fertilization, concentrate distribution per animal and farm productivity.In both groups of farms, i.e., the least and most vulnerable, respectively, farms displayed contrasting individual patterns on the X block of the PLS beyond similarities on Shannon index of the farmland, percentage of cropland, amount of fodder distributed per animal and percentage of silage in animal diets  of farmland in permanent pastures, and small areas of grass-based ley pastures (12 ± 2%) and crops (5 ± 2%). Stocking rate was among the lowest of the farm sample (0.90 LU/ha) yet farmers applied mineral fertilizers (20 kg N/ha). As for B1, concentrate distribution was limited, i.e., 0.57 t DM/LU but remained high when considering farm productivity. Reliance on grazing for animal feeding was higher, which led to only 37% of animal feed being distributed. The farmer adjusted this percentage (±12%) and the percentage of harvests as silage and consequently of silage in animal diets (±8%) according to climatic conditions of each year. Thus, farmer B2 promoted managed adaptability of his farm configuration but as for B1, he suffered from limited technical efficiency due to inconsistency between stocking rate, mineral fertilization, concentrate distribution per animal and farm productivity.B6 lay between B1 and B2, with 36% (±6%) of its area in permanent pastures vs. 61% (±11%) in grass-based ley pastures and 3% (±5%) in crops. Stocking rate was high and variable across years (1.84 LU/ha ±0.38). Grazing was central in farm organization, with only 35% (±9%) of animal feed being distributed. This farmer adapted the rate of mineral nitrogen fertilization (40 kg N/ha ±25) and the amount of concentrate distributed (0.86 t DM/LU ±23%) over the years. As for B1, farmer B6 promoted managed adaptability of his farm configuration but suffered from efficiency issues similar to B1 and B2.Figure 8). Farm D8 had 30% of its area dedicated to silage maize production against 13% as natural pastures and 43% as grass-based ley pastures. Stocking rate was high (2.05 LU/ha) and stable across years. To sustain this stocking rate, and try to secure fodder production, the farmer applied 87 kg N/ha/year. In this farm, to uncertainty and risks related to grazing, 83% (±4%) of animal feed was distributed, 80% in the form of silage. Concentrate supplementation  was efficient compared to local standards . Farmer D8 did not implement adaptations over the years, keeping all (surveyed) practices constant despite climatic and economic variability. He rather counted on the technical efficiency and robustness of his farm configuration.Similarly, the farms considered the least vulnerable  displayed differences in farm configurations and adaptations implemented by farmers . Yet, nitrogen fertilization was high (66 kg N/ha) and stable. The percentage of distributed feed was limited to 70% (±3%) of animal intake including 78.4% of silage. Again, concentrate supplementation  was efficient compared to local standards. The main difference compared to D8 was that this farmer reacted to contextual variability and adjusted the stocking rate (±18%) and the duration of animal off-farm grazing (±224%) according to annual conditions. Farmer D7 developed these two options to promote managed adaptability of his farm configuration.Farm M2 differed from D7 and D8 in that the farmer relied on diversity of his farm configuration to self-regulate the impacts of climatic and economic variability. Farm M2 was a mixed farm with both beef and dairy cattle. This farm had only 12% of its area dedicated to silage maize production but included a wide range of crops (32% of its area), cover crops (1%) and pasture types . A higher rate of concentrate supplementation  was another source of security for farm productivity against variability. Stocking rate was high (1.61 LU/ha) and stable across years and fertilization was limited (20 kg N/ha). Like farmer D8, farmer M2 did not implement adaptations over the years but counted on robustness and self-adaptability of his farm configuration.Interannual variability in climatic conditions and economic conditions had no significant effect on vulnerability variables. This was because differences in initial farm configurations and farmers’ technical adaptations were more discriminant than interannual variability in climatic conditions and input or output prices. Moreover, distribution of farms within the factorial design of the PLS was mainly determined by farms’ initial positions rather than by transitions that occurred during the survey period. This reveals initial differences in farm vulnerability were partly decreased through adaptive capacity, but the technical adaptations implemented did not fully compensate for initial differences. Among the attributes of adaptive capacity identified by Another reason for this result relates to the farm sample. Farm adaptations implemented during the survey period were rather limited. Most farms remained quite similar to their initial configuration. Thus it was not ideal to illustrate the potential of the proposed method to characterize adaptation pathways that reduce most cattle-farm vulnerability to climatic and economic variability. We are currently applying this method on farms operating a conversion to organic farming that implies implementation of more significant adaptations than that of farms surveys for the purpose of this article. Moreover, our sample of farms combined beef, dairy and mixed farms. Patterns of combinations between farmer practices and economic performance might differ among farm types. For this reason, even when changing economic indicators , economic performance was not related to specific practices. This could be the reason why we did not explain economic performance whereas we do explain it on other analysis focused on dairy farms that we are currently conducting.Despite differences in exposure to climatic and economic variability among sample farms, it emerged that functional diversity of the farmland tended to reduce farm vulnerability to climatic and economic variability, as already observed by No specific combination integrating all studied farmers’ practices emerged that reduces cattle-farm vulnerability to climatic and economic variability. Instead, we observed that different combinations led to similar levels of vulnerability. In these systems, the practices implemented  were consistent with the objective of developing the properties targeted , as discussed by These results confirm earlier findings  showing Longitudinal analysis is a complex and multistage process that can be performed using several approaches. Most scientific work on vulnerability and related concepts including resilience  and adapIn the area of field-based vulnerability assessments applied to agricultural systems, the integrated method we developed is original in several aspects:(i) Farm vulnerability is often reduced to productivity or income issues  and baseFigure 2). For instance, we analyzed our case study using either raw measurements or fitted values. Results of both were quite similar; therefore, we presented only results of raw measurements, but those of fitted values would be more relevant when de-noised data is needed. If fitted values are preferred, the intercept of the corresponding linear regression can replace the mean level. One could also consider a more complex function  instead of a linear mixed model as a straightforward extension. In the study presented in this article, the low number of data per farm over time limited the choice to a linear trend, in addition to the ease of interpretation of this parameter. Moreover, the selected vulnerability and explanatory variables are not a rigid list. Instead, it is possible to fully adapt the list depending on the local context to include vulnerability variables representing, e.g., farmer workload and explanatory variables representing, e.g., farmer knowledge and experience.(ii) Unlike previously published quantitative methodologies for assessing farm vulnerability , our metThus, our method does not force users to use a given set of vulnerability variables, remaining adaptable to the specific characteristics of each project.(iii) Whereas many studies are limited to a single contextual change, mainly climate , and the(iv) Many studies focus on strategic changes and consequently use multi-year time steps [5 years in (v) Whereas vulnerability assessment methods are not always oriented toward practice , our metOur method offers insight to unravel the complex issue of cattle-farm vulnerability to climatic and economic variability. It showed that even in a small region such as Aveyron, no single combination of farmers’ practices decreases cattle-farm vulnerability to climatic and economic variability. It also revealed that system and transformational adaptations of the practices implemented  appears the only way to ensure the sustainability of the most vulnerable farms. Since it can be easily applied to other farming contexts, our method has the potential to support farmers in the development of sector-specific and locally relevant adaptations.GM and M-AM designed the research, supervised data collection, developed the methodology, performed the analysis and wrote the manuscript. Both of them contributed equally. MC supported the development of the method as well as data analysis and interpretation. She also reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.