Ecology

ALSD is widespread in Majorca
The summer of 2017, following the Xf official detection in October 2016, provided a good scenario for testing whether Xf was implicated in the almond decline. An episode of high water demand (three consecutive days of Tmax  > 33 °C and RH  More

Ethics statement
All animal experiments were performed at the Max F. Perutz Laboratories of the University of Vienna, Austria. All experiments were discussed and approved by the University of Veterinary Medicine, Vienna, Austria, and conducted in accordance with protocols approved by the Federal Ministry for Education, Science and Research of the Republic of Austria under the license number BMWF-66.006/0001-WF/V/3b/2016. Animals were randomized for interventions but researchers processing the samples and analyzing the data were aware which intervention group corresponded to which cohort of animals.
Mouse colon incubations
Three adult (6-8 weeks old) C57BL/6N mice bred at the Max F. Perutz Laboratories, University of Vienna, under SPF conditions were sacrificed per experiment, and their colon was harvested anaerobically (85% N2, 10% CO2, 5% H2) in an anaerobic tent (Coy Laboratory Products, USA). Contents from each colon were suspended in 7.8 mL of 50% D2O-containing PBS and homogenized by vortexing. Similar conditions have been successfully applied in the past to monitor activity of individual cells in gut communities without causing major changes in the activity of individual community members34. The homogenate was left to settle for 10 min, and the supernatant was then distributed into glass vials and supplemented with different concentrations of mucosal sugar monosaccharides, glucose, mucin or nothing (no-amendment control) (all amendment chemicals were from Sigma-Aldrich, except D(+)-galactose which was purchased from Carl Roth GmbH) (Fig. 1a,c). After incubation for 6 h at 37 °C, glycerol was added (to achieve a final concentration of 20% (v/v) of glycerol in the microcosms) and the vials were crimp-sealed with rubber stoppers and stored at −80 °C until further processing. Prior to glycerol addition, subsamples of the biomass were collected, pelleted and supernatants stored at −80 °C for HILIC LC-MS/MS measurements. Pellets were washed with PBS to remove D2O and were fixed in 3% formaldehyde for 2 h at 4 °C and stored in 50% PBS/50% ethanol solution at −20 °C until further use. A total of three biological replicates were established using starting material pooled from three animals each (experiments MonoA, MonoB and MonoC). For the MonoA and MonoB experiments, microcosms were established for all the different concentrations of monosaccharides tested (Fig. 1c), while for MonoC only the highest concentrations of monosaccharides tested in incubations MonoA and MonoB were supplemented. Note that analysis of mucin-amended sorted fractions has been published elsewhere35. Since mucin contains all the monosaccharides included in this study, it constitutes an important control, and therefore we processed the sequencing data from mucin sorts in parallel with our samples and included it in our analyses (Fig. 2).
Mass spectrometric analysis of mucosal monossaccharides
Hydrophilic interaction chromatography (HILIC) LC-MS/MS was used for the measurement of mucosal monosaccharides in microcosm supernatants. Frozen samples were thawed at room temperature and centrifuged for 10 min at 18.000 × g and 4 °C. Supernatants were then diluted 1:50 with acetonitrile:water (1:1; v/v) and a volume of 3 µl was injected onto the chromatographic column. The UHPLC system (UltiMate 3000, Thermo Scientific) was coupled to a triple quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific) by an electrospray ionization interface. Hydrophilic interaction chromatographic separation was realized on a Luna aminopropyl column (3 µm, 150 × 2 mm; Phenomenex, Torrance, CA) at a flow rate of 0.25 ml/min. Eluent A consisted of 95% water and 5% acetonitrile with 20 mM ammonium acetate and 40 mM ammonium hydroxide as additives and eluent B of 95% acetonitrile and 5% water. A multi-step gradient was optimized as follows: 100% B until minute 2, then linearly decreased to 80% B until minute 20 and further to 0% B until minute 25. The column was kept at 0% B for 4 min before it was equilibrated for 5 min at the initial conditions. The column temperature was maintained at 40 °C. The mass spectrometer was operated in multiple reaction monitoring (MRM) mode. Electrospray ionization (ESI) was optimized as follows: spray voltage 2800 V (positive mode) and 3000 V (negative mode); vaporizer temperature 250 °C; sheath gas pressure 30 Arb; ion sweep gas pressure 2 Arb; auxiliary gas pressure 10 Arb; capillary gas temperature 260 °C. Mass spectrometric parameters were optimized by direct injection and are reported together with the retention times of individual sugars in Supplementary Table 6. Spiking experiments and regular quality control checks were conducted to evaluate and ensure the systems’ proper performance.
Confocal Raman microspectroscopy and spectral processing of fixed samples
Formaldehyde-fixed samples were spotted on aluminum-coated slides (Al136; EMF Corporation) and washed by dipping into ice-cold Milli-Q (MQ) water (Millipore) to remove traces of buffer components. Individual cells were observed under a 100×/0.75 NA microscope objective. Single microbial cell spectra were acquired using a LabRAM HR800 confocal Raman microscope (Horiba Jobin-Yvon) equipped with a 532-nm neodymium-yttrium aluminum garnet (Nd:YAG) laser and either 300 grooves/mm diffraction grating. Spectra were acquired in the range of 400–3200 cm−1 for 30 s with 2.18 mW laser power. Raman spectra were background-corrected using the sensitive nonlinear iterative peak algorithm, and afterwards normalized to the sum of its absolute spectral intensity34. For quantification of the degree of D substitution in CH bonds (%CD), the bands assigned to C–D (2040–2,300 cm−1) and C–H (2,800–3,100 cm−1) were calculated using integration of the specified region34.
Raman-activated cell sorting
For RACS of D-labeled cells, 100 μl of glycerol-preserved microcosms containing non-fixed cells were pelleted, washed once with MQ water containing 0.3 M glycerol and finally resuspended in 0.5 ml of 0.3 M glycerol in MQ water. Cell sorting was performed in a fully automated manner using a Raman microspectroscope (LabRAM HR800, Horiba Scientific, France) combined with optical tweezers and a polydimethylsiloxane (PDMS) microfluidic sorter. The optical tweezers (1,064 nm Nd:YAG laser at 500 mW) and Raman (532 nm Nd:YAG laser at 45 mW or 80 mW; see below) laser were focused at the same position of the interface between the sample and sheath streams using a single objective (63x, 1.2 NA water-immersion, Zeiss). The in-house software based on the graphical user interface (GUI; written in MATLAB) detected the single-cell capture and its deuterium labeling status by calculating the cell index (PC = I1,620-1,670/Ifluid,1,620-1,670; where I is the integrated intensity between the indicated wavenumbers) and the labeling index (PL = I2040-2300/I1850-1900), respectively. We did not detect a significant change in the C–D peak region (2040–2300 cm−1) due the presence of 0.3 M of glycerol in the sorting fluid (added to minimize the osmotic stress when the sample was re-suspended for the RACS) (Supplementary Fig. 2a). Other spectral regions (e.g., 2700 cm−1) were slightly affected, but the sorting algorithm employed and the parameters described above take these small changes into account: the cell index PC (I1620–1670/Ifluid,1620–1670) used to detect single-cell capture was calculated by comparing the Raman intensity of cells measured in real-time to that of the working fluid measured in the calibration (conducted before the actual sorting). The threshold value for PL (I2040–2300/I1850–1900) was chosen based on the measurement of the control sample (i.e., sample incubated in non-D2O-containing medium). We used two software versions, each of which uses 45 mW (version 1) and 80 mW (version 2) Raman laser powers, respectively. The second version operates with higher power based on the addition of a laser shutter that blocks the Raman laser while the cells are being translocated, reducing the laser-induced damage on the cell. This version allows shorter acquisition times to be employed, and therefore higher throughput of the platform. The laser power for each version was chosen based on visual inspection of captured cells as described35. For the NeuAc and GlcNAc-amendment sorts (version 1, since version 2 was not yet available), PC value was calculated from cell spectra acquired for 2 s at the “capture location”, while the PL value was calculated from spectra obtained with a 5 s exposure time at the “evaluation location”. Fucose, GalNAc, and galactose-supplemented sorts were performed with version 2 of the platform, which in the meantime became available, significantly reducing sorting times. For these sorts both PC and PL values were simultaneously measured at the “capture location” with a 0.3 s exposure time. Only the D-labeled cells were translocated to the ‘evaluation location’ and immediately released. In order to determine the threshold PL above which a cell from the microcosms should be considered D-labeled (and therefore selected and sorted), cells from glucose-supplemented microcosms incubated in the absence or presence of D (0% versus 50% D2O in the microcosms) were run on the platform prior to sorting. The threshold PL number can vary across microcosms due to different microbial compositions and/or physiological status of cells present in the starting material, as well as due to different laser powers employed. Therefore we determined the PL threshold separately for both MonoA and MonoB incubations using both 45 and 80 mW laser power. Nevertheless, we reached a PL threshold of 6.19 for all sets of conditions and incubations tested (Supplementary Fig. 2b). We speculate this was due to the identical conditions used in both incubations and the fact that both communities have a similar microbial composition (Fig. 1e). To test the sorting accuracy of the platform on our samples, the negative control (H2O, glucose-supplemented microcosm) was re-run in the platform and sorted using the adopted threshold (PL = 6.19) (Supplementary Table 1). As expected, no cells were considered labeled by the platform under these conditions. Sorted fractions were nevertheless collected and sequenced as controls.
Preparation of 16S rRNA gene amplicon libraries and 16S rRNA gene sequence analyses
DNA extracted from the mouse colon microcosms or from mouse fecal pellets using a phenol-chloroform bead-beating protocol52 was used as a template for PCR. PCR amplification was performed with a two-step barcoding approach53. In the first-step PCR, the 16S rRNA gene of most bacteria was targeted using oligonucleotide primers (Supplementary Table 7) containing head adaptors (5′-GCTATGCGCGAGCTGC-3′) in order to be barcoded in a second step PCR53. Barcode primers consisted of the 16 bp head sequence and a sample-specific 8 bp barcode from a previously published list at the 5′ end. The barcoded amplicons were purified with the ZR-96 DNA Clean-up Kit (Zymo Research, USA) and quantified using the Quant-iT PicoGreen dsDNA Assay (Invitrogen, USA). An equimolar library was constructed by pooling samples, and the resulting library was sent for sequencing on an Illumina MiSeq platform at Microsynth AG (Balgach, Switzerland). Paired-end reads were quality-filtered and processed using QIIME 153,54. Reads were then clustered into operational taxonomic units (OTUs) of 97% sequence identity and screened for chimeras using UPARSE implemented in USEARCH v8.1.186155. OTUs were classified using the RDPclassifier v2.1256 as implemented in Mothur v1.39.557 using the Silva database v13258. Sequencing libraries were rarefied and analyzed using the vegan package (v2.4-3) of the software R (https://www.r-project.org/, R 3.4.0).
Sequencing of mouse gut isolates
Bacteroides sp. Isolate FP24 was isolated from YCFA agar plates (DSMZ medium 1611- YCFA MEDIUM (modified)) by plating ten-fold dilution series of a microcosms supplemented with 2 mg/ml of NeuAc (experiment MonoB)). Escherichia sp. isolate FP11 and Anaerotruncus sp. isolate FP23 were isolated from C. difficile minimal medium17 agar plates supplemented with 0.25% NeuAc or 0.25% GlcNAc by plating ten-fold dilution series of a microcosms supplemented with 2 mg/ml of NeuAc (experiment MonoB) or of a microcosms supplemented with 5 mg/ml of GlcNAc (experiment MonoA), respectively. Colonies were re-streak on the same medium plates until complete purity. Pure colonies were grown overnight in 10 mL of BHI medium (Brain heart infusion at 37 g per liter of medium) supplemented with: yeast extract, 5 g; Na2CO3, 42 mg; cysteine, 50 mg; vitamin K1, 1 mg; hemin, 10 µg. DNA was extracted from pelleted biomass using the QIAGEN DNAeasy Tissue and Blood kit (Qiagen, Austin, TX, USA) according to the manufacturer´s instructions. Sequencing libraries were prepared using the NEBNext® Ultra™ II FS DNA kit (Illumina) and sequenced in an Illumina MiSeq platform with 300-bp paired-end sequencing chemistry (Joint Microbiome Facility, University of Vienna and Medical University of Vienna, Austria). Reads were quality trimmed with the bbduk option of BBmap (v 34.00) at phrad score 15. Quality-trimmed reads were assembled with SPAdes (v 3.11.1)59. For isolate FP11, assembled reads were subsequently, iteratively (n = 6) reassembled with SPAdes using contigs of >1 kb from the previous assembly as “trusted contigs” for input and iterating kmers from 11 to 121 in steps of 10. CheckM (v1.0.6) assessment60 of these genomes is summarized in Supplementary Data 1.
Mini-metagenome sequencing and genomic analyses
Labeled RACS cells were collected into PCR tubes, lysed and subjected to whole-genome amplification using the Repli-g Single Cell Kit (QIAGEN), according to the manufacturer’s instructions. Shotgun libraries were generated using the amplified DNA from WGA reactions (sorted fractions) or DNA isolated using the phenol-chloroform method (initial microcosms) as a template and Nextera XT (Illumina) reagents. Libraries were sequenced with a HiSeq 3000 (Illumina) in 2 × 150 bp mode at the Biomedical Sequencing Facility, Medical University of Vienna, Austria. The sequence reads were quality trimmed and filtered using BBMap v34.00 (https://sourceforge.net/projects/bbmap/). The remaining reads were assembled de novo using SPAdes 3.11.159 in single-cell mode (k-mer sizes: 21, 35, 55). Binning of the assembled reads into metagenome-assembled genomes (MAGs) was performed with MetaBAT 2 (v2.12.1)61 using the following parameters: minContig 2000, minCV 1.0, minCVSum 1.0, maxP 95%, minS 60, and maxEdges 200. The quality and contamination of all MAGs were checked with CheckM 1.0.660 (Supplementary Data 1). MAGs >200 kb obtained from all samples were compared and de-replicated using dRep 1.4.362. Automatic genome annotation of contigs >2 kb within each de-replicated MAG was performed with RAST 2.063. Taxonomic classification of each MAG was obtained using GTDB-Tk64 (v0.1.3, gtdb.ecogenomic.org/).
The relative abundance of each MAG on the initial microcosms was calculated based on metagenomic coverage. Filtered reads from each sequenced microcosm were mapped competitively against all retrieved MAGs using BBMap (https://sourceforge.net/projects/bbmap/). Read coverage was normalized by genome size and relative abundances of each genome in each sample were calculated based on the formula: covA = (bpA/gA)/(bpT/gT), where covA is the relative abundance of MAG A on a particular sample, bpA is the number of base pairs from reads matching MAG A, gA is the genome length of MAG A, bpT is the total number of base pairs from reads matching all MAGs recovered from that particular sample and gT is the sum of all MAGs genome lengths.
For determination of the presence of encoded enzymes for catabolism of mucin monosaccharides among MAGs, predicted protein sequences from recovered MAGs were subject to local BLASTP analyses65, against a custom database. The database was composed of all enzymes involved initial hydrolysis and catabolism of mucosal sugar monosaccharides (Supplementary Data 2), which were previously curated from a total of 395 human gut bacteria15. A strict e-value threshold of 10−50 was used for all BLASTP analyses. During initial setup of the analysis pipeline, functional assignments of proteins that gave positive BLASTP hits were manually verified by examining annotations from RAST 2.0 and by performing BLASTP analyses against the NCBI-nr database (NCBIBlast 2.2.26).
To verify the enrichment of a selected dataset of mucin-degrading enzymes35 in the assemblies derived from sorted fractions, BLASTX analyses of scaffolds from each fraction as well as from the initial microcosms metagenomes (unsorted) were performed against the selected mucin-degrading enzyme sequences15 (Supplementary Table 3). An e-value threshold of 10−50 was also used for all BLASTX analyses.
Phylogenomic analyses
A concatenated marker alignment of 34 single-copy genes was generated for all MAGs using CheckM 1.0.660 and the resulting alignment was used to calculate a tree with the approximate maximum-likelihood algorithm of FastTree 2.1.1066. Phylogenomic trees were visualized and formatted using iTOL v4 (https://itol.embl.de/). In order to identify the closest relative for each MAG, the query MAG and close reference genomes (based on the generated phylogenomic tree) were compared using dRep 1.4.362. Compared genomes with a whole-genome-based average nucleotide identity (ANIm) >99%39 were considered to be the same population genome.
High-resolution mass spectrometric analyses
Glycerol-preserved biomass (150 μL) from microcosm incubations was pelleted and suspended in 50 μL of lysis buffer (1% sodium dodecyl sulfate (SDS), 10 mM TRIS base, pH 7.5). Protein lysates were subjected to SDS polyacrylamide gel electrophoresis followed by in-gel tryptic digestion. Proteins were stained with colloidal Coomassie Brilliant Blue G-250 (Roth, Kassel, Germany) and detained with Aqua dest. Whole protein bands were cut into gel pieces and in-gel-digestion with trypsin 30 µL (0.005 µg/µL) was performed overnight. Extracted peptides where dried and resolved in 0.1% formic acid and purified by ZipTip® treatment (EMD Millipore, Billerica, MA, USA).
In total, 5 µg of peptides were injected into nanoHPLC (UltiMate 3000 RSLCnano, Dionex, Thermo Fisher Scientific), followed by separation on a C18-reverse phase trapping column (C18 PepMap100, 300 µm × 5 mm, particle size 5 µm, nano viper, Thermo Fischer Scientific), followed by separation on a C18-reverse phase analytical column (Acclaim PepMap® 100, 75 µm × 25 cm, particle size 3 µm, nanoViper, Thermo Fischer Scientific). Mass spectrometric analysis of eluted peptides where performed on a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with a TriVersa NanoMate (Advion, Ltd., Harlow, UK) source in LC chip coupling mode. LC Gradient, ionization mode and mass spectrometry mode were performed as described before67. Briefly, peptide lysate were injected into a Nano-HPLC and trapped in a C18-reverse phase column (Acclaim PepMap® 100, 75 µm × 2 cm, particle size 3 µM, nanoViper, Thermo Fisher) for 5 min. Peptide separation was followed by a two-step gradient in 90 min from 4 to 30% of B (B: 80% acetonitrile, 0.1% formic acid in MS-grade water) and then 30 min from 30 to 55% of B. The temperature of the separation column was set to 35 °C and the flow rate was 0.3 µL/min. The eluted peptides were ionized and measured. The MS was set to a full MS/dd-MS2 mode scan with positive polarity. The full MS scan was adjusted to 120,000 resolution, the automatic gain control (AGC) target of 3 × 106 ions, maximum injection time for MS of 80 s and a scan range of 350 to 1550 m/z. The dd-MS2 scan was set to a resolution of 15,000 with the AGC target of 2 × 105 ions, a maximum injection time for 120 ms, TopN 20, isolation window of 1.6 m/z, scan range of 200 to 2000 m/z and a dynamic exclusion of 30 s.
Raw data files were converted into mzML files and searched with MS-GF + against a database obtained from microcosm metagenomes composed of 276,284 predicted protein-encoding sequences. The following parameters were used for peptide identification: enzyme specificity was set to trypsin with one missed cleavage allowed using 10 ppm peptide ion tolerance and 0.05 Da MS/MS tolerance. Oxidation (methionine) and carbamidomethylation (cysteine) were selected as modifications. False discovery rates (FDR) were determined with the node Percolator68. Proteins were considered as identified when at least one unique peptide passed a FDR of 5%.
The MetaProSIP toolshed69 embedded in the Galaxy framework70 (v2.3.2, http://galaxyproject.org/) was used to identify the incorporation of stable isotopes into peptides. MetaProSIP calculates the relative isotope abundance (RIA) on detected isotopic mass traces (m/z tolerance of ±10 ppm, intensity threshold of 1000, and an isotopic trace correlation threshold of 0.7).
In vitro growth experiments
A. muciniphila strain Muc (DSM 22959), Ruthenibacterium lactatiformans strain 585-1 (DSM 100348) and Alistipes timonensis strain JC136 (DSM 25383) were obtained from DSMZ. Muribaculum intestinale strain YL27 (DSM 28989) was kindly provided by Prof. Bärbel Stecher (Max-von-Pettenkofer Institute, LMU Munich, Germany). Bacteroides sp. FP24 was isolated from YCFA agar plates (DSMZ medium 1611-YCFA MEDIUM (modified)). All strains were grown in reduced A II medium71 consisting of (per liter of medium): BHI, 18.5 g; yeast extract, 5 g; trypticase soy broth, 15 g; K2HPO4, 2.5 g; hemin, 10 µg; glucose, 0.5 g; Na2CO3, 42 mg; cysteine, 50 mg; menadione, 5 µg; fetal calf serum (complement-inactivated), 3% (vol/vol). For A. muciniphila cultivation, the growth medium was supplemented with 0.025% (w/v) of mucin. C. difficile was grown in BHI medium (37 g per liter of medium) supplemented with: yeast extract, 5 g; Na2CO3, 42 mg; cysteine, 50 mg; vitamin K1, 1 mg; hemin, 10 µg. All strains were grown at 37 °C under anaerobic conditions until stationary phase, and then serially diluted and plated into media agar plates in order to determine the number of viable cells present in 1 ml of stationary phase-culture. For mixed-growth experiments, the culture volume equivalent to 1 × 106 CFU of each strain was pelleted, cells were washed with PBS, mixed in equal proportions and finally resuspended in 100 μl of PBS. This bacterial mixture containing a total of 5 × 106 BacMix cells was then used to inoculate 2.5 ml of A II medium (diluted two fold in 2× PBS) supplemented or not with 0.25% (10 mM) carbon source (0.125% or 4 mM of NeuAc and 0.125% or 6 mM of GlcNAc). After 12 h, the same tube was inoculated with 1 × 106 C. difficile CFU and bacterial growth was followed by measuring the OD at 600 nm every hour until stationary phase. At three distinct points of the C. difficile growth curve—lag (t12, right after C. difficile addition), mid-exponential (t18) and early stationary phase (t21)—a sample aliquot was collected and ten-fold dilutions were plated in a C. difficile selective medium72. This selective medium (CCFA) includes antibiotics such as cycloserine and cefoxitn at concentrations that are inhibitory to most gut organisms, except for C. difficile, allowing to determine total C. difficile counts. A second aliquot was immediately pelleted and the pellet was stored at −80 °C for RNA extraction.
Quantitative PCR of C. difficile 16S rRNA gene copy number density
DNA was extracted from 100 mg of mouse fecal pellet using the QIAGEN DNAeasy Tissue and Blood kit (Qiagen, Austin, TX, USA) according to the manufacturer´s instructions, with an additional step of mechanical cell disruption by bead beating (30 s at 6.5 m/s) right after addition of kit lysis buffer AL. Extracted DNA (2 μl) was subjected to quantitative PCR using 0.2 μM of primers specifically targeting the C. difficile 16S rRNA gene73 (Supplementary Table 7) and 1× SYBR green Master Mix (Bio-Rad) in a total reaction volume of 20 μl. Standard curves were generated from DNAs extracted from fecal pellets of SPF (uninfected) mice spiked in with different known numbers of C. difficile cells (102, 103, 104, 105, 106, 107, and 108) as described in Kubota et al., 2014. Amplification and detection were performed using a CFX96™ Real-Time PCR Detection System (Bio-Rad) using the following cycling conditions: 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 56 °C for 20 s, and 72 °C for 30 s. To determine the specificity of PCR reactions, melt curve analysis was carried out after amplification by slow cooling from 95 to 60 °C, with fluorescence collection at 0.3 °C intervals and a hold of 10 s at each decrement. Only assays with amplification efficiencies above 80% were considered for analysis.
RNA extraction and quantitative real-time PCR
Total nucleic acids (TNA) were extracted from mouse fecal pellets or from in vitro cultures using a phenol-chloroform bead-beating protocol52. RNA was purified from DNAse-treated TNA fractions using the GeneJET Cleanup and Concentration micro kit (Thermo Fisher Scientific). cDNA was synthesized from 0.5 μg of total RNA with 1 μl of random hexamer oligonucleotide primers. Samples were heated for 5 min at 70 °C. After a slow cooling, 2 μl of deoxynucleoside triphosphates (dNTP; 2.5 mM each), 40 units of recombinant ribonuclease inhibitor (RNaseOUT) and 4 μl of reverse transcription (RT) buffer were added and cDNAs were synthesized for 2 h at 50 °C using 200 units SuperScript™ III Reverse Transcriptase (all reagents used in cDNA synthesis were from Thermo Fisher Scientific). Real-time quantitative PCR was performed in a 20-μl reaction volume containing 2 μl of cDNA, 1x SYBR green Master Mix (Bio-Rad) and 0.2 μM of gene-specific C. difficile primers targeting the following genes: DNA polymerase III PolC-type dnaF74, nanA, nanT17 and nagB (this work; Supplementary Table 7). Amplification and detection were performed as described above. In each sample, the quantity of cDNAs of a gene was normalized to the quantity of cDNAs of the C. difficile DNA polymerase lII gene74 (dnaF). The relative change in gene expression was recorded as the ratio of normalized target concentrations (threshold cycle [ΔΔCT] method75). Fold changes were normalized to in vitro growths in C. difficile minimal medium containing 0.5% glucose17. To determine the specificity of PCR reactions, melt curve analysis was carried out after amplification by slow cooling from 95 to 60 °C, with fluorescence collection at 0.3 °C intervals and a hold of 10 s at each decrement. Only assays with amplification efficiencies above 80% were considered for analysis.
Murine in vivo adoptive transfer experiments
Female C57BL/6N 6-8 weeks old mice (n = 33 total) were purchase from Janvier Labs. Animals were kept in isolated, ventilated cages under specific pathogen-free conditions at the animal facility of the Max F. Perutz Laboratories, University of Vienna, Austria, with controlled temperature of 21 ± 1 °C and humidity of 50 ± 10%, in a 12-h light/dark cycle. Mice received a standard diet (V1124-300; Ssniff, Soest, Germany) and autoclaved water ad libitum. Mice were administered antibiotics (0.25 mg/ml clindamycin (Sigma-Aldrich) for six days in drinking water) and subsequently assigned randomly to one of two groups. One day following antibiotic cessation, mice from each group were split into 3 cages (to minimize the cage effect) and each mouse received either 5,000,000 CFU of a 5-bacteria suspension (BacMix, containing equal numbers of A. muciniphila strain Muc (DSM 22959), Ruthenibacterium lactatiformans strain 585-1 (DSM 100348), Alistipes timonensis strain JC136 (DSM 25383), Muribaculum intestinale strain YL27 (DSM 28989), and Bacteroides sp. isolate FP24) or vehicle (PBS) by gavage (Fig. 5a). At the time of BacMix and BacMixC administration, the mouse diet was switched from a standard diet (V1124-300; Ssniff, Soest, Germany) to a isocaloric polysaccharide-deficient chow76 with sucrose but no cellulose or starch (Ssniff, Soest, Germany). For the BacMixC adoptive transfer, each mouse (n = 10 per group) received 5,000,000 CFU of a 3-bacteria suspension (BacMixC, containing equal numbers of Anaerotruncus sp. isolate FP23; Lactobacillus hominis strain DSM 23910 and of Escherichia sp. isolate FP11) or vehicle (PBS) by gavage (Supplementary Fig. 7). One day after BacMix or BacMixC administration, mice were challenged with 1,000,000 CFU of C. difficile strain 630 deltaErm77. A. muciniphila, R. lactatiformans and M. intestinale were grown in reduced A II medium (supplemented with 0.025% mucin for A. muciniphila). Anaerotruncus sp. isolate FP23, A. timonensis and Bacteroides sp. isolate FP24 were grown in PYG (DSMZ medium 104). Lactobacillus hominis and Escherichia sp. isolate FP11 were grown in YCFA medium (DSMZ medium 1611). C. difficile was grown in BHI medium (37 g per liter of medium). All bacteria were grown under anaerobic conditions (5% H2, 10% CO2, rest N2) at 37 °C and resuspended in anaerobic PBS prior to administration to animals. C. difficile titers were quantified in fecal samples obtained from mice 24, 48, 72, and 120 h after infection by overnight cultivation in C. difficile selective agar plates72. Animals were monitored throughout the entire experiment and weight loss was recorded.
Measurement of mucus thickness and goblet cell volume
Segments of mouse colon (approximately 10 mm long) were fixed in 2% PFA in phosphate-buffered saline (PBS) for 12 h at 4 °C. Samples were washed with 1× PBS and then stored in 70% ethanol at 4 °C until embedding. For embedding, samples were immersed in 3 changes of xylene for 1 h each, then immersed in 3 changes of molten paraffin wax (Paraplast, Electron Microscopy Sciences) at 56–58 °C for one hour each. Blocks were allowed to harden at room temperature. Sections were cut to 4 μm thickness using a Leica microtome (Leica Microsystems) and were floated on a water bath at 40–45 °C, then transferred to slides, dried, and stored at room temperature. In preparation for staining, slides were de-paraffinized by heating at 60 °C for 30 min followed by immersion in 2 changes of xylene for 5 min each, then in 2 changes of 100% ethanol for 1 minute each, then rehydrated through 80%, and 70% ethanol for 1 minute each. Slides were then dipped in water, drained, air-dried and a drop of Alcian Blue stain (Sigma Aldrich) applied on top. Samples were incubated with Alcian Blue for 20 min at room temperature and then washed in water to remove the excess of stain. Samples were mounted in Vectashield Hardset™ Antifade Mounting Medium (Vector Laboratories) and visualized using a Leica Confocal scanning laser microscope (Leica TCS SP8X, Germany). For measurements of the width of the mucus layer and determination of goblet cell volume per crypt, ImageJ software (https://imagej.nih.gov/ij/, 1.48 v) was used.
Histology and histopathological scoring
Mouse colons were flushed with cold PBS to remove all contents, and the entire colon was rolled into Swiss rolls. The tissues were fixed in 2% PFA for 12 h at 4 °C and then washed in 1x PBS and transferred to 70% ethanol until embedding. Swiss rolls were embedded in the same manner as described above for segments of mouse colon. Paraffin-embedded, PFA fixed tissues were sectioned at 4.5 μm. Tissue sections were de-paraffinized and hematoxylin and eosin stained (Mayer’s Hematoxylin, Thermo Scientific; Eosin 1%, Morphisto, Germany). Histopathological analyses were performed using a semi-quantitative scoring system78 that evaluated the severity of crypt damage and cellular infiltration, epithelial erosion and tissue thickening using a severity score from 0 to 3 (0 = intact, 1 = mild, 2 = moderate, 3 = severe), and those scores were multiplied by a score for percent involvement (0 = 0%, 1 = 1–25%, 2 = 26–50%, 3 = 50–100%). A trained and blinded scientist performed the scoring. Representative images were acquired using an Olympus CKX53 microscope and Olympus SC50 camera.
Quantification of C. difficile toxin TcdB
Levels of TcdB in mouse colon contents were quantified relative to a standard curve of purified TcdB using an ELISA assay kit (“Separate detection of C. difficile toxins A and B”, TGC Biomics) according to the manufacturer’s instructions. For each mouse, approximately 10 mg of colon content were used in the assay. The limit of detection for the assay in our conditions was determined to be 5.14 ng of TcdB per gram of colon content (Supplementary Fig. 8c). One of the mice from the BacMix group had toxin levels below the detection limit and was therefore excluded from analysis.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Bowman, W.D., Hacker, S.D., Cain, M.L. Ecology, 4th Edn. (Sinauer Press, 2017).
2.
Eggleston, D. B., Lipcius, R. N. & Hines, A. H. Density-dependent predation by blue crabs upon infaunal clam species with contrasting distribution and abundance patterns. Mar. Ecol. Progr. Ser. 85, 55–68 (1992).
ADS  Article  Google Scholar 

3.
Boström-Einarsson, L., Bonin, M. C., Munday, P. L. & Jones, G. P. Strong intraspecific competition and habitat selectivity influence abundance of a coral-dwelling damselfish. J. Exp. Mar. Biol. Ecol. 448, 85–92 (2013).
Article  Google Scholar 

4.
Ruggerone, G.T., Zimmermann, M., Myers, K.W., Nielsen, J.L., & Rogers, D.E. Competition between Asian pink salmon (Oncorhynchus gorbuscha) and Alaskan sockeye salmon (O. nerka) in the North Pacific Ocean. Fish. Oceanogr. 12, 209–219 (2003).

5.
Greer, A. L., Briggs, C. J. & Collins, J. P. Testing a key assumption of host-pathogen theory: Density and disease transmission. Oikos 117, 1667–1673 (2008).
Article  Google Scholar 

6.
Turchin, P. Does population ecology have general laws?. Oikos 94, 17–26 (2001).
Article  Google Scholar 

7.
Yenni, G., Adler, P. B. & Ernest, S. M. Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461 (2012).
PubMed  Article  Google Scholar 

8.
Weis, A. E., Simms, E. L. & Hochberg, M. E. Will plant vigor and tolerance be genetically correlated? Effects of intrinsic growth rate and self-limitation on regrowth. Evol. Ecol. 14, 331–352 (2000).
Article  Google Scholar 

9.
Marino, A., Rodríguez, V. & Pazos, G. Resource-defense polygyny and self-limitation of population density in free-ranging guanacos. Behav. Ecol. 27, 757–765 (2016).
Article  Google Scholar 

10.
Chamaillé-Jammes, S., Fritz, H., Valeix, M., Murindagomo, F. & Clobert, J. Resource variability, aggregation and direct density dependence in an open context: The local regulation of an African elephant population. J. Anim. Ecol. 77, 135–144 (2008).
PubMed  Article  Google Scholar 

11.
Westoby, M. The self-thinning rule. Adv. Ecol. Res. 14, 167–225 (1984).
Article  Google Scholar 

12.
Sedinger, J. S., Herzog, M. P., Person, B. T., Kirk, M. T., Obritchkewitch, T., Martin, P. P., & Bosque, C. Large-scale variation in growth of Black Brant goslings related to food availability. Auk 118, 1088–1095 (2001).

13.
Marschall, E. A. & Crowder, L. B. Density-dependent survival as a function of size in juvenile salmonids in streams. Can. J. Fish. Aquat. Sci. 52, 136–140 (1995).
Article  Google Scholar 

14.
Zheng, X., Huang, L., Huang, B. & Lin, Y. Factors regulating population dynamics of the amphipod Ampithoe valida in a eutrophic subtropical coastal lagoon. Acta Oceanol. Sin. 32, 56–65 (2013).
ADS  CAS  Article  Google Scholar 

15.
Li, G. Y., & Zhang, Z. Q. Does size matter? Fecundity and longevity of spider mites (Tetranychus urticae) in relation to mating and food availability. Syst. Appl. Acarol.-UK 23, 1796–1808 (2018).

16.
Niu, H., Zhao, L. & Sun, J. Phenotypic plasticity of reproductive traits in response to food availability in invasive and native species of nematode. Biol. Inv. 15, 1407–1415 (2013).
Article  Google Scholar 

17.
Cannizzo, Z. J., Lang, S. Q., Benitez-Nelson, B. & Griffen, B. D. An artificial habitat increases the reproductive fitness of a range-shifting species within a newly colonized ecosystem. Sci. Rep. 10, 1–13 (2020).
Article  CAS  Google Scholar 

18.
Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Annu. Rev. Ecol. Evol. Syst. 32, 95–126 (2001).
Article  Google Scholar 

19.
Strayer, D. L., D’Antonio, C. M., Essl, F., Fowler, M. S., Geist, J., Hilt, S., & Latzka, A. W. Boom‐bust dynamics in biological invasions: Towards an improved application of the concept. Ecol. Lett. 20, 1337–1350 (2017).

20.
Jaćimović, M. et al. Boom-bust like dynamics of invasive black bullhead (Ameiurus melas) in Lake Sava (Serbia). Fish. Manag. Ecol. 26, 153–164 (2019).
Article  Google Scholar 

21.
Alcorlo, P., Geiger, W., & Otero, M. Reproductive biology and life cycle of the invasive crayfish Procambarus clarkii (Crustacea: Decapoda) in diverse aquatic habitats of South-Western Spain: Implications for population control. Fund. Appl. Limnol./Arch. Hydrobiol. 173, 197–212 (2008).

22.
Melero, Y., Robinson, E., & Lambin, X. Density-and age-dependent reproduction partially compensates culling efforts of invasive non-native American mink. Biol. Invasions 17, 2645–2657.

23.
Yoshida, K., Hoshikawa, K., Wada, T. & Yusa, Y. Patterns of density dependence in growth, reproduction and survival in the invasive freshwater snail Pomacea canaliculata in Japanese rice fields. Freshw. Biol. 58, 2065–2073 (2013).
Article  Google Scholar 

24.
Williams, A. B. & McDermott, J. J. An eastern United States record for the western Indo-Pacific crab, Hemigrapsus sanguineus (Crustacea: Decapoda: Grapsidae). Proc. Biol. Soc. Wash. 103, 108–109 (1990).
Google Scholar 

25.
Breton, G., Faasse, M., Noël, P. & Vincent, T. A new alien crab in Europe: Hemigrapsus sanguineus (Decapoda: Brachyura: Grapsidae). J. Crustacean Biol. 22, 184–189 (2002).
Article  Google Scholar 

26.
Blakeslee, A. M., Kamakura, Y., Onufrey, J., Makino, W., Urabe, J., Park, S., & Miura, O. Reconstructing the invasion history of the Asian shorecrab, Hemigrapsus sanguineus (De Haan 1835) in the Western Atlantic. Mar. Biol. 164, 47 (2017).

27.
Lohrer, A. M. & Whitlatch, R. B. Interactions among aliens: Apparent replacement of one exotic species by another. Ecology 83, 719–732 (2002).
Article  Google Scholar 

28.
Kraemer, G. P., Sellberg, M., Gordon, A. & Main, J. Eight-year record of Hemigrapsus sanguineus (Asian shore crab) invasion in western Long Island Sound estuary. Northeast. Nat. 14, 207–224 (2007).
Article  Google Scholar 

29.
Epifanio, C. E. Invasion biology of the Asian shore crab Hemigrapsus sanguineus: A review. J. Exp. Mar. Biol. Ecol. 441, 33–49 (2013).
Article  Google Scholar 

30.
Lord, J. P. & Williams, L. M. Increase in density of genetically diverse invasive Asian shore crab (Hemigrapsus sanguineus) populations in the Gulf of Maine. Biol. Invasions 19, 1153–1168 (2017).
PubMed  PubMed Central  Article  Google Scholar 

31.
Brousseau, D. J., Kriksciun, K. & Baglivo, J. A. Fiddler crab burrow usage by the Asian crab, Hemigrapsus sanguineus, in a Long Island Sound salt marsh. Northeast. Nat. 10, 415–420 (2003).
Article  Google Scholar 

32.
O’Connor, N. J. Invasion dynamics on a temperate rocky shore: from early invasion to establishment of a marine invader. Biol. Invasions 16, 73–87 (2014).
Article  Google Scholar 

33.
O’Connor, N. J. Changes in population sizes of Hemigrapsus sanguineus (Asian Shore Crab) and resident crab species in southeastern New England (2010–2016). Northeast. Nat. 25, 197–201 (2018).
Article  Google Scholar 

34.
Schab, C. M., Park, S., Waidner, L. A. & Epifanio, C. E. Return of the native: Historical comparison of invasive and indigenous crab populations near the mouth of Delaware Bay. J. Shellfish Res. 32, 751–758 (2013).
Article  Google Scholar 

35.
Bloch, C. P., Curry, K. D., Fisher-Reid, M. C. & Surasinghe, T. D. Population Decline of the Invasive Asian Shore Crab (Hemigrapsus sanguineus) and Dynamics of Associated Intertidal Invertebrates on Cape Cod, Massachusetts. Northeast. Nat. 26, 772–784 (2019).
Article  Google Scholar 

36.
Kraemer, G. P. Changes in population demography and reproductive output of the invasive Hemigrapsus sanguineus (Asian Shore Crab) in the Long Island Sound from 2005 to 2017. Northeast. Nat. 26, 81–94 (2019).
Article  Google Scholar 

37.
Stentiford, G. D., Bateman, K. S., Dubuffet, A., Chambers, E., & Stone, D. M. Hepatospora eriocheir (Wang and Chen, 2007) gen. et comb. nov. infecting invasive Chinese mitten crabs (Eriocheir sinensis) in Europe. J. Invertebr. Pathol. 108, 156–166 (2011).

38.
Bateman, A. W., Buttenschön, A., Erickson, K. D. & Marculis, N. G. Barnacles vs bullies: Modelling biocontrol of the invasive European green crab using a castrating barnacle parasite. Theor. Ecol. 10, 305–318 (2017).
Article  Google Scholar 

39.
Bojko, J., Stebbing, P. D., Dunn, A. M., Bateman, K. S., Clark, F., Kerr, Stewart-Clark, S., Johannesen, Á., & Stentiford, G. D. Green crab Carcinus maenas symbiont profiles along a North Atlantic invasion route. Dis. Aquat. Organ. 128, 147–168 (2018).

40.
Jensen, G. C., McDonald, P. S. & Armstrong, D. A. East meets west: competitive interactions between green crab Carcinus maenas, and native and introduced shore crab Hemigrapsus spp. Mar. Ecol. Progr. Ser. 225, 251–262 (2002).
ADS  Article  Google Scholar 

41.
DeRivera, C. E., Ruiz, G. M., Hines, A. H. & Jivoff, P. Biotic resistance to invasion: Native predator limits abundance and distribution of an introduced crab. Ecology 86, 3364–3376 (2005).
Article  Google Scholar 

42.
Kim, A. K. & O’Connor, N. J. Early stages of the Asian shore crab Hemigrapsus sanguineus as potential prey for the striped killifish Fundulus majalis. J. Exp. Mar. Biol. Ecol. 346, 28–35 (2007).
Article  Google Scholar 

43.
Brousseau, D. J., Murphy, A. E., Enriquez, N. P. & Gibbons, K. Foraging by two estuarine fishes, Fundulus heteroclitus and Fundulus majalis, on juvenile Asian shore crabs (Hemigrapsus sanguineus) in Western Long Island Sound. Estuar. Coast. 31, 144–151 (2008).
Article  Google Scholar 

44.
Savaria, M. C. & O’Connor, N. J. Predation of the non-native Asian shore crab Hemigrapsus sanguineus by a native fish species, the cunner (Tautogolabrus adspersus). J. Exp. Mar. Biol. Ecol. 449, 335–339 (2013).
Article  Google Scholar 

45.
Griffen, B. D. & Delaney, D. G. Species invasion shifts the importance of predator dependence. Ecology 88, 3012–3021 (2007).
PubMed  Article  Google Scholar 

46.
Keogh, C. L., Miura, O., Nishimura, T. & Byers, J. E. The double edge to parasite escape: invasive host is less infected but more infectable. Ecology 98, 2241–2247 (2017).
PubMed  Article  Google Scholar 

47.
Kroft, K. L. & Blakeslee, A. M. Comparison of parasite diversity in native panopeid mud crabs and the invasive Asian shore crab in estuaries of northeast North America. Aquat. Invasions 11, 287–301 (2016).
Article  Google Scholar 

48.
Blakeslee, A. M., Keogh, C. L., Byers, J. E., Lafferty, A. M. K. K. D. & Torchin, M. E. Differential escape from parasites by two competing introduced crabs. Mar. Ecol. Progr. Ser. 393, 83–96 (2009).
ADS  Article  Google Scholar 

49.
Lohrer, A. M., Fukui, Y., Wada, K. & Whitlatch, R. B. Structural complexity and vertical zonation of intertidal crabs, with focus on habitat requirements of the invasive Asian shore crab, Hemigrapsus sanguineus (de Haan). J. Exp. Mar. Biol. Ecol. 244, 203–217 (2000).
Article  Google Scholar 

50.
Ledesma, M. E. & O’Connor, N. J. Habitat and diet of the non-native crab Hemigrapsus sanguineus in southeastern New England. Northeast. Nat. 8, 63–78 (2001).
Article  Google Scholar 

51.
Brousseau, D. J. & Goldberg, R. Effect of predation by the invasive crab Hemigrapsus sanguineus on recruiting barnacles Semibalanus balanoides in western Long Island Sound, USA. Mar. Ecol. Progr. Ser. 339, 221–228 (2007).
ADS  Article  Google Scholar 

52.
Brousseau, D. J. & Baglivo, J. A. Laboratory investigations of food selection by the Asian shore crab, Hemigrapsus sanguineus: Algal versus animal preference. J. Crustacean Biol. 25, 130–134 (2005).
Article  Google Scholar 

53.
Griffen, B. D. Linking individual diet variation and fecundity in an omnivorous marine consumer. Oecologia 174, 121–130 (2014).
ADS  PubMed  Article  Google Scholar 

54.
Riley, M. E., Vogel, M. & Griffen, B. D. Fitness-associated consequences of an omnivorous diet for the mangrove tree crab Aratus pisonii. Aquat. Biol. 20, 35–43 (2014).
Article  Google Scholar 

55.
Griffen, B. D. & Norelli, A. P. Spatially variable habitat quality contributes to within-population variation in reproductive success. Ecol. Evol. 5, 1474–1483 (2015).
PubMed  PubMed Central  Article  Google Scholar 

56.
Griffen, B. D. & Riley, M. E. Potential impacts of invasive crabs on one life history strategy of native rock crabs in the Gulf of Maine. Biol. Invasions 17, 2533–2544 (2015).
Article  Google Scholar 

57.
Belgrad, B. A. & Griffen, B. D. The influence of diet composition on fitness of the blue crab, Callinectes sapidus. PLoS ONE 11, e0145481 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Griffen, B. D., Vogel, M., Goulding, L. & Hartman, R. Energetic effects of diet choice by invasive Asian shore crabs: Implications for persistence when prey are scarce. Mar. Ecol. Progr. Ser. 522, 181–192 (2015).
ADS  Article  Google Scholar 

59.
Griffen, B. D. The timing of energy allocation to reproduction in an important group of marine consumers. PLoS ONE 13, e0199043 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

60.
Guiñez, R., Petraitis, P. S. & Castilla, J. C. Layering, the effective density of mussels and mass-boundary curves. Oikos 110, 186–190 (2005).
Article  Google Scholar 

61.
Bertness, M. D., Gaines, S. D. & Yeh, S. M. Making mountains out of barnacles: The dynamics of acorn barnacle hummocking. Ecology 79, 1382–1394 (1998).
Article  Google Scholar 

62.
Guiñez, R. & Castilla, J. C. An allometric tridimensional model of self-thinning for a gregarious tunicate. Ecology 82, 2331–2341 (2001).
Article  Google Scholar 

63.
Alunno-Bruscia, M., Petraitis, P. S., Bourget, E. & Fréchette, M. Body size–density relationship for Mytilus edulis in an experimental food-regulated situation. Oikos 90, 28–42 (2000).
Article  Google Scholar 

64.
Weller, D. E. A reevaluation of the‐3/2 power rule of plant self‐thinning. Ecol. Monogr. 57, 23–43 (1987).

65.
Griffen, B. D. & Byers, J. E. Community impacts of two invasive crabs: the interactive roles of density, prey recruitment, and indirect effects. Biol. Invasions 11, 927–940 (2009).
Article  Google Scholar 

66.
Lohrer, A. M. & Whitlatch, R. B. Relative impacts of two exotic brachyuran species on blue mussel populations in Long Island Sound. Mar. Ecol. Progr. Ser. 227, 135–144 (2002).
ADS  Article  Google Scholar 

67.
Nelson, K. Scheduling of reproduction in relation to molting and growth in malacostracan crustaceans. Crustacean Egg Product. 7, 77–116 (1991).
Google Scholar 

68.
Kibria, G. Studies on molting, molting frequency and growth of shrimp Penaeus monodon fed on natural and compounded diets. Asian Fish. Sci. 6, 203–211 (1993).
Google Scholar 

69.
Petit, H., Nègre-Sadargues, G., Castillo, R. & Trilles, J. P. The effects of dietary astaxanthin on growth and moulting cycle of postlarval stages of the prawn, Penaeus japonicus (Crustacea, Decapoda). Comp. Biochem. Physiol. A Physiol. 117, 539–544 (1997).
Article  Google Scholar 

70.
Clark, R. M., Zera, A. J. & Behmer, S. T. Nutritional physiology of life-history trade-offs: How food protein–carbohydrate content influences life-history traits in the wing-polymorphic cricket Gryllus firmus. J. Exp. Biol. 218, 298–308 (2015).
PubMed  Article  Google Scholar 

71.
Rosa, R., Calado, R., Narciso, L. & Nunes, M. L. Embryogenesis of decapod crustaceans with different life history traits, feeding ecologies and habitats: A fatty acid approach. Mar. Biol. 151, 935–947 (2007).
Article  Google Scholar 

72.
Hines, A. H. Allometric constraints and variables of reproductive effort in brachyuran crabs. Mar. Biol. 69, 309–320 (1982).
Article  Google Scholar 

73.
Sorte, C. J., Davidson, V. E., Franklin, M. C., Benes, K. M., Doellman, M. M., Etter, R. J., & Menge, B. A. Long‐term declines in an intertidal foundation species parallel shifts in community composition. Global Change Biol.23, 341–352 (2017).

74.
Goedknegt, M. A., Havermans, J., Waser, A. M., Luttikhuizen, P. C., Velilla, E., Camphuysen, K. C., & Thieltges, D. W. Cross-species comparison of parasite richness, prevalence, and intensity in a native compared to two invasive brachyuran crabs. Aquat. Invasions12, 201–212 (2017).

75.
Latham, A. & Poulin, R. Field evidence of the impact of two acanthocephalan parasites on the mortality of three species of New Zealand shore crabs (Brachyura). Mar. Biol. 141, 1131–1139 (2002).
Article  Google Scholar 

76.
Latham, A. D. M. & Poulin, R. Effect of acanthocephalan parasites on hiding behaviour in two species of shore crabs. J. Helminthol. 76, 323–326 (2002).
CAS  PubMed  Article  Google Scholar 

77.
Griffen, B. D., van den Akker, D., NiNuzzo, E. R., Anderson, L. III, & Vernier, A. Comparing methods for predicting the impacts of invasive species (in press).

78.
Tyrrell, M.C., & Harris, L.G. Potential impact of the introduced Asian shore crab, Hemigrapsus sanguineus, in northern New England: Diet, feeding preferences, and overlap with the green crab, Carcinus maenas. in Marine Bioinvasions: Proceedings of the First National Conference, Cambridge, MA, 24–27 January 1999 (pp. 208–220). (MIT Sea Grant College Program, 2000).

79.
Spilmont, N., Gothland, M. & Seuront, L. Exogenous control of the feeding activity in the invasive Asian shore crab Hemigrapsus sanguineus (De Haan, 1835). Aquat. Invasions 10, 327–332 (2015).
Article  Google Scholar 

80.
McDermott, J. J. The western Pacific brachyuran Hemigrapsus sanguineus (Grapsidae) in its new habitat along the Atlantic coast of the United States: reproduction. J. Crustacean Biol. 18, 308–316 (1998).
Article  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2019). https://www.R-project.org/.

82.
Griffen, B. D. & Mosblack, H. Predicting diet and consumption rate differences between and within species using gut ecomorphology. J. Anim. Ecol. 80, 854–863 (2011).
PubMed  Article  Google Scholar 

83.
Wolcott, D. L. & O’Connor, N. J. Herbivory in crabs: Adaptations and ecological considerations. Am. Zool. 32, 370–381 (1992).
Article  Google Scholar 

84.
Mattson, W. J. Jr. Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Evol. Syst. 11, 119–161 (1980).
Article  Google Scholar 

85.
Griffen, B. D., Cannizzo, Z. J. & Gül, M. R. Ecological and evolutionary implications of allometric growth in stomach size of brachyuran crabs. PLoS ONE 13, e0207416 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Gül, M. R. & Griffen, B. D. Diet, energy storage, and reproductive condition in a bioindicator species across beaches with different levels of human disturbance. Ecol. Indic. 117, 106636 (2020).
Article  Google Scholar 

87.
Vogt, G. Functional cytology of the hepatopancreas of decapod crustaceans. J. Morphol. 280, 1405–1444 (2019).
CAS  PubMed  Google Scholar 

88.
Kyomo, J. Analysis of the relationship between gonads and hepatopancreas in males and females of the crab Sesarma intermedia, with reference to resource use and reproduction. Mar. Biol. 97, 87–93 (1988).
Article  Google Scholar 

89.
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., Smith, G. M. Zero-truncated and zero-inflated models for count data. in Mixed Effects Models and Extensions in Ecology with R 261–293. (Springer, New York, 2009).

90.
Mente, E. Effect of ration level on individual food consumption, growth and protein synthesis in the shore crab Carcinus maenas. In Nutrition, Physiology and Metabolism of Crustaceans 53–67 (Science Publishers, Enfield, 2003).
Google Scholar  More

1.
Faust K, Raes J, Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.
CAS  PubMed  Article  Google Scholar 
2.
Phelan VV, Liu WT, Pogliano K, Dorrestein PC. Microbial metabolic exchange—the chemotype-to-phenotype link. Nat Chem Biol. 2011;8:26–35.
PubMed  Article  CAS  Google Scholar 

3.
Natale P, Brüser T, Driessen AJM. Sec- and Tat-mediated protein secretion across the bacterial cytoplasmic membrane: Distinct translocases and mechanisms. Biochim Biophys Acta. 2007;1778:1735–56.
PubMed  Article  CAS  Google Scholar 

4.
Holland IB. The extraordinary diversity of bacterial protein secretion mechanisms. Meth Mol Biol. 2010;619:1–20.
CAS  Article  Google Scholar 

5.
Guerrero-Mandujano A, Hernández-Cortez C, Ibarra JA, Castro-Escarpulli G. The outer membrane vesicles: Secretion system type zero. Traffic. 2017;18:425–32.
CAS  PubMed  Article  Google Scholar 

6.
Orench‐Rivera N, Kuehn MJ. Environmentally controlled bacterial vesicle‐mediated export. Cell Microbiol. 2016;18:1525–36.
PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Kim JH, Lee J, Park J, Gho YS, editors. Gram-negative and Gram-positive bacterial extracellular vesicles. Semin Cell Dev Biol. 2015;40:97–104.

8.
Schwechheimer C, Kuehn MJ. Outer-membrane vesicles from Gram-negative bacteria: biogenesis and functions. Nat Rev Microbiol. 2015;13:605–19.
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
McBroom AJ, Kuehn MJ. Release of outer membrane vesicles by Gram‐negative bacteria is a novel envelope stress response. Mol Microbiol. 2007;63:545–58.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Arntzen MO, Varnai A, Mackie RI, Eijsink VGH, Pope PB. Outer membrane vesicles from Fibrobacter succinogenes S85 contain an array of carbohydrate-active enzymes with versatile polysaccharide-degrading capacity. Environ Microbiol. 2017;19:2701–14.
CAS  PubMed  Article  Google Scholar 

11.
Nordstrom T, Blom AM, Tan TT, Forsgren A, Riesbeck K. Ionic binding of C3 to the human pathogen Moraxella catarrhalis is a unique mechanism for combating innate immunity. J Immunol. 2005;175:3628–36.
PubMed  Article  Google Scholar 

12.
Fulsundar S, Harms K, Flaten GE, Johnsen PJ, Chopade B, Nielsen KM. Gene transfer potential of outer membrane vesicles of Acinetobacter baylyi and effects of stress on vesiculation. Appl Environ Microb. 2014;80:3469–83.
Article  CAS  Google Scholar 

13.
Mashburn LM, Whiteley M. Membrane vesicles traffic signals and facilitate group activities in a prokaryote. Nature. 2005;437:422–5.
CAS  PubMed  Article  Google Scholar 

14.
Toyofuku M, Morinaga K, Hashimoto Y, Uhl J, Shimamura H, Inaba H, et al. Membrane vesicle-mediated bacterial communication. ISME J. 2017;11:1504–9.
PubMed  PubMed Central  Article  Google Scholar 

15.
Lee EY, Choi DY, Kim DK, Kim JW, Park JO, Kim S, et al. Gram‐positive bacteria produce membrane vesicles: proteomics‐based characterization of Staphylococcus aureus‐derived membrane vesicles. Proteomics. 2009;9:5425–36.
CAS  PubMed  Article  Google Scholar 

16.
Prados-Rosales R, Baena A, Martinez LR, Luque-Garcia J, Kalscheuer R, Veeraraghavan U, et al. Mycobacteria release active membrane vesicles that modulate immune responses in a TLR2-dependent manner in mice. J Clin Investig. 2011;121:1471–83.
PubMed  Article  CAS  Google Scholar 

17.
Prados-Rosales R, Brown L, Casadevall A, Montalvo-Quiros S, Luque-Garcia JL. Isolation and identification of membrane vesicle-associated proteins in Gram-positive bacteria and mycobacteria. MethodsX. 2014;1:124–9.
PubMed  PubMed Central  Article  Google Scholar 

18.
White DW, Elliott SR, Odean E, Bemis LT, Tischler AD. Mycobacterium tuberculosis Pst/SenX3-RegX3 regulates membrane vesicle production independently of ESX-5 activity. mBio. 2018;9:e00778–18.
CAS  PubMed  PubMed Central  Google Scholar 

19.
Hoffmann C, Leis A, Niederweis M, Plitzko JM, Engelhardt H. Disclosure of the mycobacterial outer membrane: cryo-electron tomography and vitreous sections reveal the lipid bilayer structure. PNAS. 2008;105:3963–7.
CAS  PubMed  Article  Google Scholar 

20.
Ganz T, Nemeth E. Iron homeostasis in host defence and inflammation. Nat Rev Immunol. 2015;15:500–10.
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Huber DL. Synthesis, properties, and applications of iron nanoparticles. Small. 2005;1:482–501.
CAS  PubMed  Article  Google Scholar 

22.
Wandersman C, Delepelaire P. Bacterial iron sources: from siderophores to hemophores. Annu Rev Microbiol. 2004;58:611–47.
CAS  PubMed  Article  Google Scholar 

23.
Morel FM, Price N. The biogeochemical cycles of trace metals in the oceans. Science. 2003;300:944–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Ram RJ, VerBerkmoes NC, Thelen MP, Tyson GW, Baker BJ, Blake RC, et al. Community proteomics of a natural microbial biofilm. Science. 2005;308:1915–20.
CAS  PubMed  Article  Google Scholar 

25.
Cao B, Shi L, Brown RN, Xiong Y, Fredrickson JK, Romine MF, et al. Extracellular polymeric substances from Shewanella sp. HRCR‐1 biofilms: characterization by infrared spectroscopy and proteomics. Environ Microbiol. 2011;13:1018–31.
CAS  PubMed  Article  Google Scholar 

26.
Vong L, Laës A, Blain S. Determination of iron–porphyrin-like complexes at nanomolar levels in seawater. Anal Chim Acta. 2007;588:237–44.
CAS  PubMed  Article  Google Scholar 

27.
Létoffé S, Nato F, Goldberg ME, Wandersman C. Interactions of HasA, a bacterial haemophore, with haemoglobin and with its outer membrane receptor HasR. Mol Microbiol. 1999;33:546–55.
PubMed  Article  Google Scholar 

28.
Tong Y, Guo M. Bacterial heme-transport proteins and their heme-coordination modes. Arch Biochem Biophys. 2009;481:1–15.
CAS  PubMed  Article  Google Scholar 

29.
Pilpa RM, Robson SA, Villareal VA, Wong ML, Phillips M, Clubb RT. Functionally distinct NEAT (NEAr Transporter) domains within the Staphylococcus aureus IsdH/HarA protein extract heme from methemoglobin. J Biol Chem. 2009;284:1166–76.
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Gat O, Zaide G, Inbar I, Grosfeld H, Chitlaru T, Levy H, et al. Characterization of Bacillus anthracis iron‐regulated surface determinant (Isd) proteins containing NEAT domains. Mol Microbiol. 2008;70:983–99.
CAS  PubMed  PubMed Central  Google Scholar 

31.
Choby JE, Skaar EP. Heme synthesis and acquisition in bacterial pathogens. J Mol Biol. 2016;428:3408–28.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Allen CE, Schmitt MP. HtaA is an iron-regulated hemin binding protein involved in the utilization of heme iron in Corynebacterium diphtheriae. J Bacteriol. 2009;191:2638–48.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Allen CE, Schmitt MP. Novel hemin binding domains in the Corynebacterium diphtheriae HtaA protein interact with hemoglobin and are critical for heme iron utilization by HtaA. J Bacteriol. 2011;193:5374–85.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Duckworth AW, Grant S, Grant WD, Jones BE, Meijer D. Dietzia natronolimnaios sp. nov., a new member of the genus Dietzia isolated from an East African soda lake. Extremophiles. 1998;2:359–66.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Mayilraj S, Suresh K, Kroppenstedt R, Saini H. Dietzia kunjamensis sp. nov., isolated from the Indian Himalayas. Int J Syst Evol Microbiol. 2006;56:1667–71.
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Li J, Chen C, Zhao G-Z, Klenk H-P, Pukall R, Zhang Y-Q, et al. Description of Dietzia lutea sp. nov., isolated from a desert soil in Egypt. Syst Appl Microbiol. 2009;32:118–23.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Fang H, Qin X-Y, Zhang K-D, Nie Y, Wu X-L. Role of the Group 2 Mrp sodium/proton antiporter in rapid response to high alkaline shock in the alkaline-and salt-tolerant Dietzia sp. DQ12-45-1b. Appl Microbiol Biotechnol. 2018;102:3765–77.
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Wang X-B, Chi C-Q, Nie Y, Tang Y-Q, Tan Y, Wu G, et al. Degradation of petroleum hydrocarbons (C6–C40) and crude oil by a novel Dietzia strain. Bioresour Technol. 2011;102:7755–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Rédei GP M9 Bacterial Minimal Medium. In: Rédei GP, editors. Encyclopedia of genetics, genomics, proteomics and informatics, 3rd edn. Dordrecht: Springer Group; 2008. pp. 484–6.

40.
Van Kessel JC, Hatfull GF. Recombineering in Mycobacterium tuberculosis. Nat Methods. 2007;4:147–52.
PubMed  Article  CAS  Google Scholar 

41.
Liang J, Jiangyang J, Nie Y, Wu X. Regulation of the alkane hydroxylase CYP153 gene in a Gram-positive alkane-degrading bacterium, Dietzia sp. strain DQ12-45-1b. Appl Environ Microbiol. 2016;82:608–19.
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Lu S, Nie Y, Tang Y-Q, Xiong G, Wu X-L. A critical combination of operating parameters can significantly increase the electrotransformation efficiency of a Gram-positive Dietzia strain. J Microbiol Methods. 2014;103:144–51.
CAS  PubMed  Article  Google Scholar 

43.
Szvetnik A, Bihari Z, Szabo Z, Kelemen O, Kiss I. Genetic manipulation tools for Dietzia spp. J Appl Microbiol. 2010;109:1845–52.
CAS  PubMed  Google Scholar 

44.
Deininger PL. Molecular cloning: a laboratory manual. Anal Biochem. 1990;186:182–3.
Article  Google Scholar 

45.
McBroom AJ, Johnson AP, Vemulapalli S, Kuehn MJ. Outer membrane vesicle production by Escherichia coli is independent of membrane instability. J Bacteriol. 2006;188:5385–92.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Prados-Rosales R, Weinrick BC, Pique DG, Jacobs WR Jr, Casadevall A, Rodriguez GM. Role for Mycobacterium tuberculosis membrane vesicles in iron acquisition. J Bacteriol. 2014;196:1250–6.
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Biochem Cell Biol. 1959;37:911–7.
CAS  Google Scholar 

48.
Keddie RM, Cure GL. The cell wall composition and distribution of free mycolic acids in named strains of coryneform bacteria and in isolates from various natural sources. J Appl Microbiol. 1977;42:229–52.
CAS  Google Scholar 

49.
Liu Y, Zhang Q, Hu M, Yu K, Fu J, Zhou F, et al. Proteomic analyses of intracellular Salmonella enterica serovar Typhimurium reveal extensive bacterial adaptations to infected host epithelial cells. Infect Immun. 2015;83:2897–906.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Calderoncelis F, Encinar JR, Sanzmedel A. Standardization approaches in absolute quantitative proteomics with mass spectrometry. Mass Spectrom Rev. 2018;37:715–37.
CAS  Article  Google Scholar 

51.
Liang J-L, Gao Y, He Z, Nie Y, Wang M, JiangYang J-H, et al. Crystal structure of TetR family repressor AlkX from Dietzia sp. strain DQ12-45-1b implicated in biodegradation of n-alkanes. Appl Environ Microbiol. 2017;83:e01447–17.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Tashiro Y, Hasegawa Y, Shintani M, Takaki K, Ohkuma M, Kimbara K, et al. Interaction of bacterial membrane vesicles with specific species and their potential for delivery to target cells. Front Microbiol. 2017;8:571.
PubMed  PubMed Central  Article  Google Scholar 

53.
Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 2014;43:D222–D6.
PubMed  PubMed Central  Article  CAS  Google Scholar 

54.
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Song H, Sandie R, Wang Y, Andrade-Navarro MA, Niederweis M. Identification of outer membrane proteins of Mycobacterium tuberculosis. Tuberculosis. 2008;88:526–44.
CAS  PubMed  Article  Google Scholar 

57.
Daffé M, Quémard A, Marrakchi H. Mycolic acids: from chemistry to biology. In: Geiger O, editors. Biogenesis of fatty acids, lipids and membranes. Cham: Springer; 2017. p. 1–36.

58.
Choi D, Kim D, Choi SJ, Lee J, Choi J, Rho S, et al. Proteomic analysis of outer membrane vesicles derived from Pseudomonas aeruginosa. Proteomics. 2011;11:3424–9.
CAS  PubMed  Article  Google Scholar 

59.
Marchand CH, Salmeron C, Bou Raad R, Meniche X, Chami M, Masi M, et al. Biochemical disclosure of the mycolate outer membrane of Corynebacterium glutamicum. J Bacteriol. 2012;194:587–97.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Daffe M, Marrakchi H. Unraveling the structure of the mycobacterial envelope. Microbiol Spectr. 2019;7:1087–95.
Article  Google Scholar 

61.
Nishiuchi Y, Baba T, Yano I. Mycolic acids from Rhodococcus, Gordonia, and Dietzia. J Microbiol Methods. 2000;40:1–9.
CAS  PubMed  Article  Google Scholar 

62.
Collins M, Goodfellow M, Minnikin D. A survey of the structures of mycolic acids in Corynebacterium and related taxa. Microbiology. 1982;128:129–49.
CAS  Article  Google Scholar 

63.
Rath P, Saurel O, Czaplicki G, Tropis M, Daffé M, Ghazi A, et al. Cord factor (trehalose 6, 6′-dimycolate) forms fully stable and non-permeable lipid bilayers required for a functional outer membrane. Biochim Biophys Acta-Biomemb. 2013;1828:2173–81.
CAS  Article  Google Scholar 

64.
Caruana JC, Walper SA. Bacterial membrane vesicles as mediators of microbe – microbe and microbe – host community interactions. Front Microbiol. 2020;11:432.
PubMed  PubMed Central  Article  Google Scholar 

65.
Rich PR, Maréchal A 8.5 electron transfer chains: structures, mechanisms and energy coupling. In: Egelman EH, editor. Comprehensive biophysics. Amsterdam: Elsevier; 2012. p. 72–93.

66.
Butaitė E, Baumgartner M, Wyder S, Kümmerli R. Siderophore cheating and cheating resistance shape competition for iron in soil and freshwater Pseudomonas communities. Nat Commun. 2017;8:1–12.
Article  CAS  Google Scholar 

67.
Zuber B, Chami M, Houssin C, Dubochet J, Griffiths G, Daffé M. Direct visualization of the outer membrane of mycobacteria and corynebacteria in their native state. J Bacteriol. 2008;190:5672–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Sani M, Houben ENG, Geurtsen J, Pierson J, De Punder K, Van Zon M, et al. Direct visualization by cryo-EM of the mycobacterial capsular layer: a labile structure containing ESX-1-secreted proteins. PLoS Pathog. 2010;6:e1000794.
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Kramer J, Özkaya Ö, Kümmerli R. Bacterial siderophores in community and host interactions. Nat Rev Microbiol. 2020;18:152–63.
CAS  PubMed  Article  Google Scholar 

70.
Rakoff-Nahoum S, Coyne MJ, Comstock LE. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr Biol. 2014;24:40–9.
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Food and Agriculture Authority. Microbiome the Missing Link. http://www.fao.org/3/ca6767en/CA6767EN.pdf (2020).
2.
Singh, B. K. & Trivedi, P. Microbiome and the future for food and nutrient security. Microbial. Biotechnol. 10, 50–53 (2017).
Article  Google Scholar 

3.
National Academies of Science, Engineering and Medicine. New Report Identifies Five Breakthroughs to Address Urgent Challenges and Advance Food and Agricultural Sciences by 2030. http://www8.nationalacademies.org/onpinews/newsitem.aspx?RecordID=25059 (2018).

4.
Stratistics Market Research Consulting. Agricultural Microbials – Global Market Outlook (2017–2026). https://www.researchandmarkets.com/research/vdth4t/global?w=4 (2020).

5.
European Commission. Farm to fork fact sheet https://ec.europa.eu/commission/presscorner/api/files/attachment/865559/factsheet-farm-fork_en.pdf.pdf (2020).

6.
Trivedi, P., Leach, J. E. & Tringe, S. G. et al. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0412-1 (2020).
Article  PubMed  Google Scholar 

7.
Qiu, Z. et al. New frontiers in agriculture productivity: optimised microbial inoculants and in situ microbiome engineering. Biotechnol. Adv. 37, 107371 (2019).
CAS  Article  Google Scholar 

8.
Kaminsky, L. M. et al. The inherent conflicts in developing soil microbial noculants. Trends Biotechnol. 37, 140–151 (2019).
CAS  Article  Google Scholar 

9.
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLOS Biol. 15, e2001793 (2017).
Article  Google Scholar  More

1.
Hebert, P. D. N., Penton, E. H., Burns, J. M., Janzen, D. H. & Hallwachs, W. T. species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. Natl. Acad. Sci. USA 101, 14812–14817 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Hajibabaei, M., Singer, G. A. C., Hebert, P. D. N. & Hickey, D. A. DNA barcoding: how it complements taxonomy, molecular phylogenetics and population genetics. Trends Genet. 23, 167–172 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Bucklin, A., Steinke, D. & Blanco-Bercial, L. DNA Barcoding of Marine Metazoa. Ann. Rev. Mar. Sci. 3, 471–508 (2011).
PubMed  Article  PubMed Central  Google Scholar 

4.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
CAS  PubMed  PubMed Central  Google Scholar 

5.
Hebert, P. D. N., Cywinska, A., Ball, S. L. & DeWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. B Biol. Sci. 270, 313–321 (2003).
CAS  Article  Google Scholar 

6.
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

8.
Porter, T. M. & Hajibabaei, M. Over 25 million COI sequences in GenBank and growing. PLoS ONE 13, e0200177 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

9.
Buhay, J. E. “COI-like” sequences are becoming problematic in molecular systematic and DNA barcoding studies. J. Crustac. Biol. 29, 96–110 (2009).
Article  Google Scholar 

10.
Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc. Natl. Acad. Sci. USA 105, 13486–13491 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Smith, M. A. et al. Wolbachia and DNA barcoding insects: Patterns, potential, and problems. PLoS ONE 7, e36514 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Mioduchowska, M., Czyz, M. J., Gołdyn, B., Kur, J. & Sell, J. Instances of erroneous DNA barcoding of metazoan invertebrates: Are universal cox1 gene primers too “universal”?. PLoS ONE 13, e0199609 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Parola, P., Paddock, C. D. & Raoult, D. Tick-borne rickettsioses around the world: emerging diseases challenging old concepts. Clin. Microbiol. Rev. 18, 719–756 (2005).
PubMed  PubMed Central  Article  Google Scholar 

14.
Perlman, S. J., Hunter, M. S. & Zchori-Fein, E. The emerging diversity of Rickettsia. Proc. R. Soc. B Biol. Sci. 273, 2097–2106 (2006).
Article  Google Scholar 

15.
Weinert, L. A. The diversity and phylogeny of Rickettsia. in Parasite diversity and diversification: evolutionary ecology meets phylogenetics (eds. Morand, S., Krasnov, B. R. & Littlewood, D. T. J.) 150–181 (Cambridge University Press, 2015). doi:https://doi.org/10.1017/CBO9781139794749.010.

16.
Weinert, L. A., Werren, J. H., Aebi, A., Stone, G. N. & Jiggins, F. M. Evolution and diversity of Rickettsia bacteria. BMC Biol. 7, 1–15 (2009).
Article  CAS  Google Scholar 

17.
Hajduskova, E. et al. “Candidatus Rickettsia mendelii”, a novel basal group rickettsia detected in Ixodes ricinus ticks in the Czech Republic. Ticks Tick. Borne. Dis. 7, 482–486 (2016).
PubMed  Article  PubMed Central  Google Scholar 

18.
Binetruy, F., Buysse, M., Barosi, R. & Duron, O. Novel Rickettsiagenotypes in ticks in French Guiana. South America. Sci. Rep. 10, 1 (2020).
Google Scholar 

19.
Kikuchi, Y., Sameshima, S., Kitade, O., Kojima, J. & Fukatsu, T. Novel clade of Rickettsia spp. from leeches. Appl. Environ. Microbiol. 68, 999–1004 (2002).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Kikuchi, Y. & Fukatsu, T. Rickettsia infection in natural leech populations. Microb. Ecol. 49, 265–271 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Galindo, L. J. et al. Combined cultivation and single-cell approaches to the phylogenomics of nucleariid amoebae, close relatives of fungi. Philos Trans R Soc B Biol Sci 374, 2019 (2019).
Article  CAS  Google Scholar 

22.
Küchler, S. M., Kehl, S. & Dettner, K. Characterization and localization of Rickettsia sp in water beetles of genus Deronectes (Coleoptera: Dytiscidae). FEMS Microbiol. Ecol. 68, 201–211 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

23.
Pilgrim, J. et al. Torix group Rickettsia are widespread in Culicoides biting midges (Diptera: Ceratopogonidae), reach high frequency and carry unique genomic features. Environ. Microbiol. 19, 4238–4255 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Gollas-Galván, T., Avila-Villa, L. A., Martínez-Porchas, M. & Hernandez-Lopez, J. Rickettsia-like organisms from cultured aquatic organisms, with emphasis on necrotizing hepatopancreatitis bacterium affecting penaeid shrimp: An overview on an emergent concern. Rev. Aquac. 6, 2014 (2014).
Article  Google Scholar 

25.
Larsson, R. A rickettsial pathogen of the amphipod Rivulogammarus pulex. J. Invertebr. Pathol. 40, 28–35 (1982).
Article  Google Scholar 

26.
Graf, F. Presence of bacteria in the posterior caecum in the intestinal lumen of the hypogean Crustacean Niphargus virei (Gammaridae: Amphipoda). Can. J. Zool. Can. Zool. 62, 1829–1833 (1984).
Article  Google Scholar 

27.
Messick, G. A., Overstreet, R. M., Nalepa, T. F. & Tyler, S. Prevalence of parasites in amphipods Diporeia spp from Lakes Michigan and Huron. USA. Dis. Aquat. Organ. 59, 159–170 (2004).
PubMed  Article  PubMed Central  Google Scholar 

28.
Winters, A. D., Marsh, T. L., Brenden, T. O. & Faisal, M. Analysis of bacterial communities associated with the benthic amphipod Diporeia in the Laurentian great lakes basin. Can. J. Microbiol. 61, 72–81 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Bojko, J. et al. Parasites, pathogens and commensals in the “low-impact” non-native amphipod host Gammarus roeselii. Paras. Vect. 10, 193 (2017).
Article  Google Scholar 

30.
Park, E., Jorge, F. & Poulin, R. Shared geographic histories and dispersal contribute to congruent phylogenies between amphipods and their microsporidian parasites at regional and global scales. Mol. Ecol. https://doi.org/10.1111/mec.15562 (2020).
PubMed  Article  PubMed Central  Google Scholar 

31.
Lagrue, C., Joannes, A., Poulin, R. & Blasco-Costa, I. Genetic structure and host-parasite co-divergence: Evidence for trait-specific local adaptation. Biol. J. Linn. Soc. 118, 344–358 (2016).
Article  Google Scholar 

32.
Řezáč, M., Gasparo, F., Král, J. & Heneberg, P. Integrative taxonomy and evolutionary history of a newly revealed spider Dysdera ninnii complex (Araneae: Dysderidae). Zool. J. Linn. Soc. 172, 764 (2014).
Article  Google Scholar 

33.
Ceccarelli, F. S., Haddad, C. R. & Ramírez, M. J. Endosymbiotic Rickettsiales (Alphaproteobacteria) from the spider genus Amaurobioides (Araneae: Anyphaenidae). J. Arachnol. https://doi.org/10.1636/joa-s-15-009 (2016).
Article  Google Scholar 

34.
Pilgrim, J. et al. Torix Rickettsia are widespread in arthropods and reflect a neglected symbiosis. Authorea https://doi.org/10.22541/au.159534851.19125003 (2020).
Article  Google Scholar 

35.
Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples: a case study on prey DNA in Antarctic krill stomachs. Front. Zool. 5, 12 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Vestheim, H., Deagle, B. E. & Jarman, S. N. Application of blocking oligonucleotides to improve signal-to-noise ratio in a PCR. Methods Mol. Biol. 687, 265–274 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Boessenkool, S. et al. Blocking human contaminant DNA during PCR allows amplification of rare mammal species from sedimentary ancient DNA. Mol. Ecol. 21, 1806–1815 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Myers, A. A. Dispersal and endemicity in gammaridean Amphipoda. J. Nat. Hist. 27, 901–908 (1993).
Article  Google Scholar 

39.
Duron, O. et al. Origin, acquisition and diversification of heritable bacterial endosymbionts in louse flies and bat flies. Mol. Ecol. 23, 2105–2117 (2014).
PubMed  Article  PubMed Central  Google Scholar 

40.
López, J. ÁR., Husemann, M., Schmitt, T., Kramp, K. & Habel, J. C. Mountain barriers and trans-Saharan connections shape the genetic structure of Pimelia darkling beetles (Coleoptera: Tenebrionidae). Biol. J. Linn. Soc. https://doi.org/10.1093/biolinnean/bly053 (2018).
Article  Google Scholar 

41.
Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 2015 (2015).
Article  CAS  Google Scholar 

42.
Dyková, I., Veverková, M., Fiala, I., Macháčková, B. & Pecková, H. Nuclearia pattersoni sp. n. (Filosea), a new species of amphizoic amoeba isolated from gills of roach (Rutilus rutilus), and its rickettsial endosymbiont. Folia Parasitol. (Praha).50, 161–170 (2003).

43.
Wang, H. L. et al. A newly recorded Rickettsia of the Torix group is a recent intruder and an endosymbiont in the whitefly Bemisia tabaci. Environ. Microbiol. https://doi.org/10.1111/1462-2920.14927 (2020).
PubMed  PubMed Central  Article  Google Scholar 

44.
Azad, A. F. & Beard, C. B. Rickettsial pathogens and their arthropod vectors. Emerg. Infect. Dis. 4, 179–186 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Raoult, D. et al. A flea-associated Rickettsia pathogenic for humans. Emerg. Infect. Dis. 7, 73–81 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Castelli, M., Sassera, D. & Petroni, G. Biodiversity of “non-model” Rickettsiales and their association with aquatic organisms. in Rickettsiales: Biology, Molecular Biology, Epidemiology, and Vaccine Development (ed. Thomas, S.) 59–91 (Springer International Publishing, 2016). doi:https://doi.org/10.1007/978-3-319-46859-4_3.

47.
Sabaneyeva, E. et al. Host and symbiont intraspecific variability: the case of Paramecium calkinsi and “Candidatus Trichorickettsia mobilis”. Eur. J. Protistol. 62, 79–94 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Schrallhammer, M. et al. “Candidatus Megaira polyxenophila” gen. nov., sp. Nov.: considerations on evolutionary history, host range and shift of early divergent rickettsiae. PLoS ONE 8, 2013 (2013).
Article  CAS  Google Scholar 

49.
MacHtelinckx, T. et al. Microbial community of predatory bugs of the genus Macrolophus (Hemiptera: Miridae). BMC Microbiol. 12, S9 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Gerth, M. et al. Green lacewings (Neuroptera: Chrysopidae) are commonly associated with a diversity of rickettsial endosymbionts. Zool. Lett. 3, 51 (2017).
Article  Google Scholar 

51.
Reeves, W. K., Kato, C. Y. & Gilchriest, T. Pathogen screening and bionomics of Lutzomyia apache (Diptera: Psychodidae) in wyoming, USA. J. Am. Mosq. Control Assoc. 24, 444–447 (2008).
PubMed  Article  PubMed Central  Google Scholar 

52.
Noda, H. et al. Bacteriome-associated endosymbionts of the green rice leafhopper Nephotettix cincticeps (Hemiptera: Cicadellidae). Appl. Entomol. Zool. 47, 217–225 (2012).
CAS  Article  Google Scholar 

53.
Goodacre, S. L., Martin, O. Y., Thomas, C. F. G. & Hewitt, G. M. Wolbachia and other endosymbiont infections in spiders. Mol. Ecol. 15, 517–527 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Gualtieri, L., Nugnes, F., Nappo, A. G., Gebiola, M. & Bernardo, U. Life inside a gall: closeness does not favour horizontal transmission of Rickettsia between a gall wasp and its parasitoid. FEMS Microbiol. Ecol. 93, 2017 (2017).
Article  CAS  Google Scholar 

55.
Rousset, F., Bouchon, D., Pintureau, B., Juchault, P. & Solignac, M. Wolbachia endosymbionts responsible for various alterations of sexuality in arthropods. Proc. R. Soc. B Biol. Sci. 250, 91–98 (1992).
ADS  CAS  Article  Google Scholar 

56.
White, J. A., Giorgini, M., Strand, M. R. & Pennacchio, F. Arthropod endosymbiosis and evolution. in Arthropod biology and evolution: molecules, development, morphology (eds. Minelli, A., Boxshall, G. & Fusco, G.) 441–477 (Springer, Berlin, Heidelberg, 2013). doi:https://doi.org/10.1007/978-3-642-36160-9_17.

57.
Minard, G., Mavingui, P. & Moro, C. V. Diversity and function of bacterial microbiota in the mosquito holobiont. Paras. Vect. 6, 146 (2013).
Article  Google Scholar 

58.
Thompson, J. R., Rivera, H. E., Closek, C. J. & Medina, M. Microbes in the coral holobiont: Partners through evolution, development, and ecological interactions. Front. Cell. Infect. Microbiol. 4, 176 (2014).
PubMed  PubMed Central  Google Scholar 

59.
Siddall, M. E., Fontanella, F. M., Watson, S. C., Kvist, S. & Erséus, C. Barcoding bamboozled by bacteria: Convergence to metazoan mitochondrial primer targets by marine microbes. Syst. Biol. 58, 445–451 (2009).
PubMed  Article  PubMed Central  Google Scholar 

60.
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 1 (2013).
Article  CAS  Google Scholar 

61.
Roger, A. J., Muñoz-Gómez, S. A. & Kamikawa, R. The origin and diversification of mitochondria. Curr. Biol. 27, 1177–1192 (2017).
Article  CAS  Google Scholar 

62.
Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R. & Hebert, P. D. N. DNA barcoding Australia’s fish species. Philos. Trans. R. Soc. B Biol. Sci. 360, 1847–1857 (2005).
CAS  Article  Google Scholar 

63.
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Thongprem, P., Davison, H. R., Thompson, D. J., Lorenzo-Carballa, M. O. & Hurst, G. D. D. Incidence and diversity of torix Rickettsia-odonata symbioses. Microb. Ecol. https://doi.org/10.1007/s00248-020-01568-9 (2020).
PubMed  Article  PubMed Central  Google Scholar 

65.
Paradis, E. pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2011). doi:https://doi.org/10.1007/978-3-540-74686-7.

67.
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Darriba, D., Taboada, G., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. in 2010 gateway computing environments workshop (GCE) 1–8 (IEEE, 2010). doi:https://doi.org/10.1109/GCE.2010.5676129.

70.
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Article  Google Scholar 

71.
Chun, J. Y. et al. Dual priming oligonucleotide system for the multiplex detection of respiratory viruses and SNP genotyping of CYP2C19 gene. Nucleic Acids Res. 35, e40 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

1.
Novakowski, N. S. Population dynamics of a beaver population in northern latitudes. Doctoral dissertation, University of Saskatchewan (1965).
2.
Naiman, R. J., Johnston, C. A. & Kelley, J. C. Alteration of North American streams by beaver. BioSci. 38, 753–762 (1988).
Article  Google Scholar 

3.
Jenkins, S. H. & Busher, P. E. Castor canadensis. Mamm. Species 120, 1–8 (1979).
Article  Google Scholar 

4.
Kassi, N. An Indigenous perspective on protecting the Canadian boreal zone. Environ. Rev. 27, 422–423 (2019).
Article  Google Scholar 

5.
Randazzo, M. L. & Robidoux, M. A. The costs of local food procurement in a Northern Canadian First Nation community: An affordable strategy to food security?. J. Hunger Environ. Nutr. 14, 662–682 (2019).
Article  Google Scholar 

6.
Willby, N.J., Law, A., Levanoni, O., Foster, G. & Ecke, F. Rewilding wetlands: beaver as agents of within-habitat heterogeneity and the responses of contrasting biota. Phil. Trans. Royal Soc. B. 373, 2017044. https://doi.org/10.1098/rstb.2017.0444 (2018).
Article  Google Scholar 

7.
Rosell, F., Bozser, O., Collen, P. & Parker, H. Ecological impact of beavers Castor fiber and Castor canadensis and their ability to modify ecosystems. Mamm. Rev. 35, 248–276 (2005).
Article  Google Scholar 

8.
Westbrook, C. J., Cooper, D. J. & Butler, D. R. Beaver hydrology and geomorphology. In Treatise on Geomorphology—Ecogeomorphology, (ed. Shroder, J.) 293–306. (Academic Press, San Diego, 2013).
Google Scholar 

9.
Westbrook, C. J., Cooper, D. J. & Baker, B. W. Beaver dams and overbank floods influence groundwater–surface water interactions of a Rocky Mountain riparian area. Water Resour. Res., 42, W06404. https://doi.org/10.1029/2005wr004560 (2006).
ADS  Article  Google Scholar 

10.
Karran, D.J., Westbrook, C.J. & Bedard-Haughn, A. Beaver-mediated water table dynamics in a Rocky Mountain fen. Ecohydrology 11, e1923. https://doi.org/10.1002/eco.1923 (2018).
Article  Google Scholar 

11.
Woo, M. K. & Waddington, J. M. Effects of beaver dams on subarctic wetland hydrology. Arctic 43, 223–230 (1990).
Article  Google Scholar 

12.
Nyssen, J., Pontzeele, J. & Billi, P. Effect of beaver dams on the hydrology of small mountain streams: Example from the Chevral in the Ourthe Orientale basin, Ardennes, Belgium. J. Hydrol. 402, 92–102 (2011).
ADS  Article  Google Scholar 

13.
Butler, D. R. & Malanson, G. P. The geomorphic influences of beaver dams and failures of beaver dams. Geomorphology 71, 48–60 (2005).
ADS  Article  Google Scholar 

14.
Polvi, L. E. & Wohl, E. The beaver meadow complex revisited—The role of beavers in post-glacial floodplain development. Earth Surf. Proc. Land. 37, 332–346 (2012).
ADS  Article  Google Scholar 

15.
Puttock, A., Graham, H. A., Cunliffe, A. M., Elliott, M. & Brazier, R. E. Eurasian beaver activity increases water storage, attenuates flow and mitigates diffuse pollution from intensively-managed grasslands. Sci. Total Environ. 576, 430–443 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Wohl, E., Lininger, K. B. & Scott, D. N. River beads as a conceptual framework for building carbon storage and resilience to extreme climate events into river management. Biogeochemistry 141, 365–383 (2018).
CAS  Article  Google Scholar 

17.
Sulla-Menashe, D., Woodcock, C. E. & Friedl, M. A. Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environ. Res. Let. 13, 014007. https://doi.org/10.1088/1748-9326/aa9b88 (2018).
Article  Google Scholar 

18.
Ireson, A. M. et al. The changing water cycle: The boreal plains ecozone of western Canada. Wiley Interdiscip. Rev. Water 2, 505–521 (2015).
Article  Google Scholar 

19.
Hood, G. A. & Bayley, S. E. Beaver (Castor canadensis) mitigate the effects of climate on the area of open water in boreal wetlands in western Canada. Biol. Conserv. 141, 556–567 (2008).
Article  Google Scholar 

20.
Dittbrenner, B. J. et al. Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation. PloS ONE 13, e0192538. https://doi.org/10.1371/journal.pone.0192538 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Thompson, I. D. Habitat needs of furbearers in relation to logging in boreal Ontario. For. Chron. 64, 251–261 (1988).
Article  Google Scholar 

23.
Boulanger, Y. et al. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Land. Ecol. 32, 1415–1431 (2017).
Article  Google Scholar 

24.
Hood, G. A., Bayley, S. E. & Olson, W. Effects of prescribed fire on habitat of beaver (Castor canadensis) in Elk Island National Park, Canada. For. Ecol. Manage. 239, 200–209 (2007).
Article  Google Scholar 

25.
Fairfax, E. & Small, E. E. Using remote sensing to assess the impact of beaver damming on riparian evapotranspiration in an arid environment. Ecohydrology 11, e1993 (2018).
Article  Google Scholar 

26.
Creed, I. F., Duinker, P. N., Serran, J. N. & Steenberg, J. W. Managing risks to Canada’s boreal zone: Transdisciplinary thinking in pursuit of sustainability. Environ. Rev. 27, 407–418 (2019).
Article  Google Scholar 

27.
Eaton, B., T. Muhly, J.T. Fisher & S-L. Potential Impacts of Beaver on Oil Sands Reclamation Success—An Analysis of Available Literature. Oil Sands Research and Information Network, University of Alberta, School of Energy and the Environment, Edmonton, Alberta. OSRIN Report No. TR-37. (2013)

28.
Tape, K. D., Jones, B. M., Arp, C. D., Nitze, I. & Grosse, G. Tundra be dammed: Beaver colonization of the Arctic. Glob. Change Biol. 24, 4478–4488 (2018).
ADS  Article  Google Scholar 

29.
Jarema, S. I., Samson, J., McGill, B. J. & Humphries, M. M. Variation in abundance across a species’ range predicts climate change responses in the range interior will exceed those at the edge: A case study with North American beaver. Glob. Change Biol. 15, 508–522 (2009).
ADS  Article  Google Scholar 

30.
Smeraldo, S. et al. Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers (Castor fiber). J. Nat. Conserv. 37, 12–20 (2017).
Article  Google Scholar 

31.
Scrafford, M. A., Avgar, T., Abercrombie, B., Tigner, J. & Boyce, M. S. Wolverine habitat selection in response to anthropogenic disturbance in the western Canadian boreal forest. For. Ecol. Manage. 395, 27–36 (2017).
Article  Google Scholar 

32.
Sinkins, P. Ecological and hydrological consequences of beaver activity in Riding Mountain National Park, Manitoba. Masters of Science Thesis, University of Manitoba (2008).

33.
Trottier, G. C. Aerial Beaver Censuses in Riding Mountain National Park, 1973–1980. Large Mamm. Syst. Stud. Rep. 11, Canadian Wildlife Service (1980).

34.
Macfarlane, W. W. et al. Modeling the capacity of riverscapes to support beaver dams. Geomorphology 277, 72–99 (2017).
ADS  Article  Google Scholar 

35.
Karran, D. J., Westbrook, C. J., Wheaton, J. M., Johnston, C. A. & Bedard-Haughn, A. Rapid surface water volume estimations in beaver ponds. Hydrol. Earth Syst. Sci. 352, 1039–1050. https://doi.org/10.5194/hess-21-1039-2017 (2017).
ADS  Article  Google Scholar 

36.
Wang, G., McClintic, L. F. & Taylor, J. D. Habitat selection by American beaver at multiple spatial scales. Animal Biotel. 7, 10. https://doi.org/10.1186/s40317-019-0172-8 (2019).
Article  Google Scholar 

37.
Touihri, M., Labbé, J., Imbeau, L. & Darveau, M. North American beaver (Castor canadensis Kuhl) key habitat characteristics: Review of the relative effects of geomorphology, food availability and anthropogenic infrastructure. Ecoscience 25, 9–23 (2018).
Article  Google Scholar 

38.
Whitfield, P. H., Shook, K. R. & Pomeroy, J. W. Spatial patterns of temporal changes in Canadian prairie streamflow using an alternative trend assessment approach. J. Hydrol. 582, 124541. https://doi.org/10.1016/j.jhydrol.2020.124541 (2020).
Article  Google Scholar 

39.
Johnston, C. A. & Naiman, R. J. Aquatic patch creation in relation to beaver population trends. Ecology 1, 1617–1621 (1990).
Article  Google Scholar 

40.
Whitfield, C. J., Baulch, H. M., Chun, K. P. & Westbrook, C. J. Beaver-mediated methane emission: The effects of population growth in Eurasia and the Americas. Ambio 44, 7–15 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Burchsted, D. & Daniels, M. D. Classification of the alterations of beaver dams to headwater streams in northeastern Connecticut, USA. Geomorphology 205, 36–50 (2014).
ADS  Article  Google Scholar 

42.
Breck, S. W., Wilson, K. R. & Andersen, D. C. Beaver herbivory and its effect on cottonwood trees: Influence of flooding along matched regulated and unregulated rivers. River Res. Appl. 19, 43–58 (2003).
Article  Google Scholar 

43.
Martell, K. A., Foote, A. L. & Cumming, S. G. Riparian disturbance due to beavers (Castor canadensis) in Alberta’s boreal mixedwood forests: Implications for forest management. Ecoscience 13, 164–171 (2006).
Article  Google Scholar 

44.
Hood, G. A. & Bayley, S. E. The effects of high ungulate densities on foraging choices by beaver (Castor canadensis) in the mixed-wood boreal forest. Can. J. Zool. 86, 484–496 (2008).
Article  Google Scholar 

45.
Caners, R. T. & Kenkel, N. C. Forest stand structure and dynamics at Riding Mountain National Park, Manitoba, Canada. Commun. Ecol. 4, 185–204 (2003).
Article  Google Scholar 

46.
Noble, B., Liu, J. & Hackett, P. The contribution of project environmental assessment to assessing and managing cumulative effects: Individually and collectively insignificant?. Environ. Manage. 59, 531–545 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

47.
Thompson, C., Mendoza, C. A. & Devito, K. J. Potential influence of climate change on ecosystems within the Boreal Plains of Alberta. Hydrol. Proc. 31, 2110–2124 (2017).
Article  Google Scholar 

48.
Lapointe St-Pierre, M., Labbé, J., Darveau, M., Imbeau, L. & Mazerolle, M. J. Factors affecting abundance of beaver dams in forested landscapes. Wetlands 37, 941–949 (2017).
Article  Google Scholar 

49.
Hillman, G. R. Flood wave attenuation by a wetland following a beaver dam failure on a second order boreal stream. Wetlands 18, 21–34 (1998).
Article  Google Scholar 

50.
Sallows, T. Beaver abundance—riding mountain dataset. Open Government, Government of Canada. https://open.canada.ca/data/en/dataset/36228fda-d442-4364-9810-cd77c376a851 (2018).

51.
Petro, V. M., Taylor, J. D., Sanchez, D. M. & Burnett, K. M. Methods to predict beaver dam occurrence in coastal Oregon. Northw. Sci. 92, 278–289 (2018).
Article  Google Scholar 

52.
Graham, H. A. et al. Modelling Eurasian beaver foraging habitat and dam suitability, for predicting the location and number of dams throughout catchments in Great Britain. Eur. J. Wildlife Res. 66, 42 (2020).
Article  Google Scholar 

53.
Richards, L.K. Elk/moose population dynamics in the Riding Mountain National Park region. Masters Thesis, University of Manitoba (1997).

54.
Morrison, A., Westbrook, C. J. & Bedard-Haughn, A. Distribution of Canadian Rocky Mountain wetlands impacted by beaver. Wetlands 35, 95–104 (2015).
Article  Google Scholar 

55.
Devito, K. J. et al. Landscape controls on long-term runoff in subhumid heterogeneous Boreal Plains catchments. Hydrol. Proc. 31, 2737–2751 (2017).
Article  Google Scholar 

56.
Westbrook, C. J., Cooper, D. J. & Anderson, C. B. Alteration of hydrogeomorphic processes by invasive beavers in southern South America. Sci. Total Environ. 574, 183–190 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. III. & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).
ADS  Article  Google Scholar 

58.
Walker, D.J. Landscape complexity and vegetation dynamics in Riding Mountain National Park, Canada. Doctoral dissertation, University of Manitoba (2002).

59.
Doucet, R. Regeneration silviculture of aspen. For. Chron. 65, 23–27 (1989).
Article  Google Scholar 

60.
McMaster, R. T. & McMaster, N. D. Composition, structure, and dynamics of vegetation in 15 beaver-impacted wetlands in western Massachusetts. Rhodora 103, 293–320 (2001).
Google Scholar 

61.
Green, K. C. & Westbrook, C. J. Changes in riparian area structure, channel hydraulics, and sediment yield following loss of beaver dams. J. Ecosyst. Manage. 10, 68–78 (2009).
Google Scholar 

62.
Price, D. T. et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365 (2013).
Article  Google Scholar 

63.
Dumanski, S., Pomeroy, J. W. & Westbrook, C. J. Hydrological regime changes in a Canadian Prairie basin. Hydrol. Proc. 29, 3893–3904 (2015).
Article  Google Scholar 

64.
Neumayer, M., Teschemacher, S., Schloemer, S., Zahner, V. & Rieger, W. Hydraulic modelling of beaver dams and evaluation of their impacts on flood events. Water 12, 300 (2020).
Article  Google Scholar 

65.
Westbrook, C. J., Ronnquist, A. & Bedard-Haughn, A. Hydrological functioning of a beaver dam sequence and regional dam persistence during an extreme rainstorm. Hydrol. Proc. 34, 3726–3737 (2020).
Article  Google Scholar 

66.
Andersen, D. C. & Shafroth, P. B. Beaver dams, hydrological thresholds, and controlled floods as a management tool in a desert riverine ecosystem, Bill Williams River, Arizona. Ecohydrology 3, 325–338 (2010).
Article  Google Scholar 

67.
Bailey, R. H. Notes on the Vegetation in Riding Mountain National Park Manitoba. Forest Management Institute (1968).

68.
Kennedy, R. E., Yang, Z. & Cohen, W. B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sens. Environ. 114, 2897–2910 (2010).
ADS  Article  Google Scholar  More

Vertical migration
We performed two separate vertical migration experiments (Fig. 1A,B). In the first one (Fig. 1A) a group of 45 krill was exposed to LD for 48 h. In the second one (Fig. 1B) a group of 41 krill was exposed to LD for 24 h followed by constant darkness (DD) for 48 h. During LD, lights were turned on at 6:00 (corresponding to Zeitgeber Time 0 or ZT0). Light intensity gradually increased until 12:00 (ZT6) and then decreased gradually until 18:00 (ZT12), when the lights were turned off. During DD, lights were turned off at all times. In both experiments, krill could move freely within a vertical column tank (200 cm height × 50 cm diameter) filled with chilled and filtered seawater (Supplementary Fig. S1). No food was offered at any time during the experiments and an acclimation period of three days (LD, no food) was provided before each experiment. We used an infrared (IR) camera system to monitor DVM during light and dark phases and estimated the variation in mean krill depth over time. We tested the presence of rhythmic patterns using the RAIN algorithm, which allows the detection of rhythms of any period and waveform23.
Figure 1

Vertical migration patterns of krill exposed to LD and LD-DD. (A) Vertical migration patterns of krill exposed to LD for 48 h. X-axis indicate the Zeitgeber Time (ZT) given in hours from each event of lights-on (6:00), corresponding to ZT0. Y-axis indicate the mean krill depth in cm (n = 45). Error bars represent SEMs. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). (B) Vertical migration patterns of krill exposed to LD for 24 h followed by 48 h in DD. X-axis represent the time in hours from the beginning of the run. For the first 24 h, time is given in ZT, with ZT0 corresponding to lights-on (6:00). For the following 48 h, time is given in Circadian Time or CT, with CT0 corresponding to subjective lights-on in DD (6:00). Y-axis indicate the mean krill depth in cm (n = 41). Error bars represent SEMs. White, grey and light grey rectangles represent alternation of light, dark and subjective light phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

Clear rhythmic oscillations were detected in both experiments. When krill were exposed to LD, they displayed rhythmic vertical migration with a period of about 24 h. Peak upward migration occurred towards the second half of the light phase. In the first experiment (Fig. 1A), RAIN detected a period of oscillation of 24 h (p-value = 3.5e−07) in day 1 and a period of 23 h (p-value = 5.4e−08) in day 2, with peak upward migration at ZT9 in both days. In the second experiment (Fig. 1B), RAIN detected a period of 24 h (p-value = 4.2e−04) in day 1, when krill were exposed to LD, with peak upward migration at ZT8.
Krill DVM in the field displays mostly a nocturnal migratory pattern, characterized by upward migration during the night and downward migration during the day6,11. Therefore, we were at first surprised to observe the opposite DVM pattern in the lab, with upward migration during the day (i.e. light phase) and downward migration during the night (i.e. dark phase). However, previous studies on Drosophila already revealed that circadian behaviors might differ significantly between wild and laboratory populations of the same species24. Physical constriction, exposure to artificial light regimes and feeding schedules and deprivation of ecological interactions may have a profound impact on the behavior of captured animals25. In the aquarium, krill are fed mostly during the day. This, in association with the absence of predators, might have led to the reversal of the DVM rhythm. In addition, in order to avoid interaction with rhythmic cues related to food, krill were briefly starved before and during the experiment. Previous studies suggested that starved zooplankton might become more attracted towards light sources26,27. This may have contributed to stimulate the upward movement observed during the light phases. Additional observations of DVM with (A) krill accustomed to different feeding schedules (e.g. krill fed during the night) and (B) starved krill exposed to illuminated dark phases (DL or LL) might help to clarify these points.
After we switched to DD conditions (Fig. 1B), during the first day we did not observe any significant rhythm. This might not necessarily imply that no rhythmic migration occurred. In fact, individual krill might have been still rhythmic, but they might have not been synchronized with each other due to the absence of the LD entraining cue or Zeitgeber. A similar phenomenon has already been reported for wild zooplankton in the Arctic and Antarctic during periods of continuous illumination in summer (i.e. during midnight sun)28,29. At that time of the year, in the Arctic, the local populations of Calanus finmarchicus and C. glacialis did not display any net DVM as revealed by backscatter data, but unsynchronized individual migrations were registered instead29. Implementation of individual tracking methods to apply in the lab during DVM monitoring under extreme photoperiodic conditions including constant darkness (DD) and constant light (LL) would allow further insight into this aspect.
During the second day of DD exposure, RAIN detected a period of 12 h (p-value = 2.3e−02), with a first peak of migration in the early subjective light phase (CT29) and a second peak in the early dark phase (CT41). This suggested that in free-running conditions an endogenous rhythm of vertical migration might emerge with a period of about 12 h. This is in agreement with previous studies of swimming activity, oxygen consumption, enzyme activity and transcription in krill exposed to DD16,18,22 and strongly suggests that the endogenous clock in krill is characterized by a free-running period significantly shorter than 24 h.
Oxygen consumption
An endogenous rhythm of activity occurring at the organismic level might contribute to the occurrence of DVM in zooplankton19,30. This might apply to krill as well, since they are known to display daily rhythms in metabolism and physiology15,16. Therefore, we monitored daily rhythms of oxygen consumption in krill exposed to LD and DD and associated them with the observed DVM patterns. Due to technical limitations, we could not monitor oxygen consumption directly within the vertical migration tank. We used individual krill incubated in 2 L Schott bottles filled with oxygen-saturated chilled and filtered seawater instead. We followed the decrease in pO2 over a period of 48 h and tested the presence of rhythmic consumption patterns using the RAIN algorithm. Clear rhythmic oscillations were detected in both conditions.
In LD, two out of six tested animals (33%) displayed rhythmic oxygen consumption (Fig. 2A,B). Both individuals displayed clear oscillations in the circadian range (i.e., approx. 24 h period): the first one (krill#4, Fig. 2A) displayed a period of 21 h in day 1 (p-value = 1.9e−04) and 24 h in day 2 (p-value = 3.7e−02), while the second one (krill#6, Fig. 2B) displayed a period of 24 h in both days (day 1, p-value = 5.3e−11; day 2, p-value = 3.9e−11). The oscillation was always strongly synchronized to the LD cycle, with peak oxygen consumption occurring at ZT11 (day 1) and ZT12 (day 2) in krill#4 (Fig. 2A) and ZT12 (day 1) and ZT13 (day 2) in krill#6 (Fig. 2B), corresponding to the light/dark transitions. Considering that the animals were incubated within a small volume of water (2 L) and their ability of swim freely might have been severely reduced, changes in swimming activity might not have greatly contributed to the oxygen uptake oscillation. Therefore, this could be interpreted as the result of an internal metabolic oscillation instead. This would support the hypothesis that an internal rhythm of activity might contribute to DVM in krill, even if the mechanisms still remain unknown. In the Calanoid copepod Calanus finmarchicus similar oscillations of oxygen consumption were interpreted as a metabolic anticipation of DVM19.
Figure 2

Oxygen consumption patterns of krill exposed to LD. (A,B) In both panels, X-axis represents time given as Zeitgeber Time or ZT measured in hours. ZT0 corresponds to each event of lights-on (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

In DD, four out of seven tested animals (57%) displayed rhythmic oxygen consumption (Fig. 3A–D). Three of them (krill#3, #4 and #7, Fig. 3B–D) showed a period of about 24 h. The peak of oxygen uptake was advanced compared to LD and occurred during the subjective light phase. Krill#3 (Fig. 3B) displayed a period of 23 h in day 1 (p-value = 1.0e−07) and 24 h in day 2 (p-value = 4.2e−06), with peak oxygen consumption at CT11 and CT34. Krill#4 (Fig. 3C) displayed a period of 21 h in day 1 (p-value = 2.0e−05) and 24 h in day 2 (p-value = 1.6e−06), with peak consumption at CT9 and CT28. Krill#7 (Fig. 3D) displayed a period of 24 h in day 2 (p-value = 2.2e−07), with peak consumption at CT28. A fourth individual (krill#2, Fig. 3A) displayed a period of 12 h in day 1 (p-value = 1.7e−03), with a first peak of consumption at CT3 during the early subjective light phase and a second peak 12 h later (CT15) during the early dark phase. The general advance of the oxygen uptake peak observed in krill#3, 4 and 7 together with the 12 h rhythm displayed by krill#2 suggested that in free-running DD conditions a process of period shortening similar to the one observed for DVM might occur also for oxygen consumption. Previous studies suggested that in krill similar 12 h free-running endogenous rhythms might occur also at the level of metabolic gene expression and enzymatic activity16. This, together with the recent analysis of krill circadian transcriptome revealing a significant 12 h free-running oscillation at the molecular level, strongly suggests that a short endogenous period of oscillation might characterize the circadian clock of Antarctic krill.
Figure 3

Oxygen consumption patterns of krill exposed to DD. (A–D) In all panels, X-axis represents time given as Circadian Time or CT measured in hours. CT0 corresponds to first subjective lights-on event (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. Light grey and grey rectangles represent alternation of subjective light and dark phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

The relatively small percentages (33% in LD and 57% in DD) of individual krill showing significant rhythmic oxygen consumption might have been related to the small sample size in our experiment (n = 6 in LD and n = 7 in DD). However, additional measurements of krill oxygen consumption performed using the same methods in LD and DD on larger samples (n = 10), on board of RV Polarstern during Antarctic expedition PS 112 (March to May 2018), displayed similar percentages of rhythmic individuals (3 out of 10 or 30% in LD, and 4 out of 10 or 40% in DD) (Supplementary Figs. S2A–C and S3A–D). Interestingly, the on-board analyses revealed the same tendency to develop shorter periods of oscillation in DD (krill#7: T = 12 h, p-value = 8.5e−04; krill#9: T = 17 h, p-value = 8.3e−08) (Supplementary Fig. S3C,D). Also, on-board measurements in LD displayed almost a reversed phase of oscillation compared to those in the lab, with peak oxygen consumption occurring at the beginning of the light phase, around ZT4 (Supplementary Fig. S2A–C). It is tempting to speculate that this might be related to the reverse DVM pattern observed in the lab. Unfortunately, we do not have the corresponding DVM measurements from the field to support this hypothesis. Additional field observations of krill rhythms of oxygen consumption in association with DVM would help to clarify how these two phenomena might be related to each other.
Dissection of putative circadian oscillators in krill brain and eyestalks
It might be possible that the DVM rhythm and the metabolic oscillation observed in krill were under the influence of separated circadian oscillators. This was suggested by the different responses displayed by DVM and oxygen consumption in DD. While DVM showed a complete shift to a 12 h rhythm, oxygen consumption only showed an advance in the peaking time.
Previous studies on the circadian system of Crustaceans identified multiple oscillators located in the head and along the body31. In particular, a model has been proposed where the circadian oscillators in the head are situated in the brain, in the eyestalks and in the retinae of the compound eye (Supplementary Fig. S4)31. To investigate this hypothesis we compared, for the first time, daily patterns of clock genes expression in brain and eyestalks tissue of krill exposed to LD and DD. Krill were sampled within a 72-h time-series, 9 animals were collected every four hours. During the first 24 h (ZT0-24), krill were exposed to LD, while during the remaining 48 h (CT0-48) krill were exposed to DD. We dissected brain and eyestalks tissue (Supplementary Fig. S5) and measured relative changes in clock-related mRNAs over time during the first day in LD (ZT0-24) and during the second day in DD (CT24-48) (Supplementary Table S1). We used RAIN to check for rhythmic oscillations within tissue.
Clear rhythms of oscillation were found in both tissues. In LD, the core clock components in the brain displayed an antiphase relationship between positive (clk-cyc) and negative (per-tim) regulators, with clk-cyc peaking around ZT16-20 (clk: T = 24 h, p-value = 4.86e−07; cyc: T = 24 h, p-value = 2.28e−12) and per-tim peaking around ZT4 (per: T = 24 h, p-value = 2.99e−05; tim: T = 24 h, p-value = 1.31e−11) (Fig. 4A). In the eyestalks such antiphase relationship was not present and most clock genes showed upregulation during the dark phase (ZT16-24) (Fig. 4B). The antiphase relationship is a typical feature of the circadian clock and is well-known from studies on model organisms like Drosophila and mouse2. So far, previous studies on krill failed to demonstrate a clear antiphase relationship between positive and negative regulators21,22. This was discussed as a non-canonical feature of krill circadian clock, as observed before in other Crustacean species32,33. However, our results suggest that this might be related to the specific tissue analyzed. In fact, previous works focused mostly on the eyestalks or on the full head21,22, possibly failing to detect the antiphase oscillation in the brain. This indicates that separate oscillators might be present in the brain and the eyestalks of krill, as already suggested for other Crustaceans31, and suggests that the central pacemaker might be located in the brain. The oscillator in the eyestalks, where all clock genes displayed upregulation during the dark phase, seems to be mostly influenced by the external photoperiod and might be considered as a peripheral oscillator.
Figure 4

Comparison of daily patterns of clock genes expression in LD between brain and eyestalk tissues. (A, B) Heatmaps representing up- (in yellow) and down- (in blue) regulation of clock genes expression over time in the brain (A) and eyestalks (B) of krill exposed to LD. For gene names abbreviations please see Supplementary Table S1. Zeitgeber Time (ZT) given in hour from time of lights-on (6:00), corresponding to ZT0, is indicated below each column of the heatmaps. Heatmaps were generated using the heatmap.2 function in the “gplots” package (version 3.0.4, https://github.com/talgalili/gplots) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

As in LD, also in DD different patterns of regulations were observed in the brain and in the eyestalks (Supplementary Fig. S6A,B). In the brain, clk, cyc and per, three of the core clock components, displayed a marked tendency towards a shortening of the oscillation period (Fig. 5A–C). Clk shifted from 24 to 12 h (p-value = 1.31e−16), cyc from 24 to 16 h (p-value = 2.48e−10) and per from 24 to 12 h (p-value = 4.05e−07). In the eyestalks, only per showed a similar tendency, shifting from 24 h (p-value = 3.69e−06) to 16 h (p-value = 2.36e−13) (Fig. 5D). This again suggested that separated oscillators might be present in krill brain and eyestalks. The shortening of clock gene oscillation period observed in DD in the brain and, to a lesser extent, in the eyestalks, together with the short (12–16 h) oscillation period registered for cry2 across all tissues and conditions (brain LD: T = 12 h, p-value = 2.4e−08; brain DD: T = 12 h, p-value = 1.4e−04; eyestalk LD: T = 12 h, p-value = 3.2e−05; eyestalk DD: T = 16 h, p-value = 6.1e−13) (Supplementary Fig. S7A,B), is in agreement with previous observations of krill clock gene expression in DD22 and strongly indicates that krill endogenous circadian period might be significantly shorter than 24 h also at the molecular level.
Figure 5

LD-DD shortening of the oscillation period in the core clock genes clk, cyc and per. (A–C) Line-plots representing changes in expression levels over time for the clock genes clk (A), cyc (B) and per (C) in LD (blue) and DD (red) in the brain, and for the clock gene per (D) in LD (blue) and DD (red) in the eyestalks. Time intervals are reported on the x-axis. For LD, Zeitgeber Time (ZT) is used, given in hours from the time of lights-on (6:00). For DD, Circadian Time (CT) is used, given in hours from the beginning of the subjective light phase (6:00). Mean gene expression levels (n = 7) are reported on the y-axis as mean Relative Quantities (RQ), indicating the average normalized expression levels of the target clock genes relative to the expression levels of the selected internal and external reference genes at each time interval. Error bars represents SEMs (n = 7). Results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). A schematic representation of the light/dark cycle is given below the graphs. For LD, withe rectangles indicate light phases, grey rectangles indicate dark phases. For DD, light grey rectangles indicate subjective light phases, grey rectangles indicate dark phases. The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

The presence of separated oscillators in krill might help to coordinate different aspects of daily rhythmicity on different levels including physiology (e.g. oxygen consumption) and behavior (e.g. DVM). This might allow a flexible regulation of daily rhythms in response to local changes in environmental conditions driven by temporary and/or seasonal factors including photoperiod, food availability and presence/absence of predators among others.
Adaptive significance of the short endogenous free-running period in krill
The tendency in krill to display a 12 h endogenous free-running period might represent a circadian adaptation for living at high latitudes, where the photoperiodic signal displays strong seasonal variability16,22. Surveys from plants and insects along broad latitudinal gradients indicate that high-latitude species tend to have shorter endogenous periods compared to low-latitude ones34. The reasons why this happens are not fully understood yet, but it has been proposed that the short endogenous period might help the clock to entrain to a wider range of photoperiods34. Indeed, krill displayed rhythmic clock gene activity during the midnight sun in summer, suggesting that their clock can successfully entrain to extremely long photoperiods17,35. However, exposition to similar extreme photoperiods in the laboratory apparently caused the disruption of the clock, suggesting that in the field krill might switch to alternative Zeitgebers (i.e. entraining cues) to entrain the clock when photoperiod becomes extremely long/short (e.g. midsummer/midwinter)36.
From an ecological perspective, having a short endogenous period might help krill to adapt to different environmental scenarios. In the presence of overt day/night cycles, the clock would entrain to the photoperiod and promote daily rhythms with a period of approx. 24 h, like for example the nocturnal DVM pattern usually observed during spring and autumn. This would help krill to anticipate the day/night cycle and maximize the costs/benefits balance between time spent feeding at the surface and time spent hiding in deeper water layers. At the same time, the clock would promote 24 h oscillations in krill metabolism and physiology and coordinate phases of high and low activity with phases of upward and downward migration during DVM.
On the other hand, in the absence of overt day/night cues, for example during summer and winter, the clock would tend to shift towards the free-running period and promote daily rhythms with a period shorter than 24 h. Observations of krill DVM in the field during winter are scarce, due to the harsh weather conditions which characterize the Southern Ocean at that time of the year. Much of the rhythmic biology of krill during winter still remains unknown. At the same time, field observations of krill DVM and swimming activity during summer suggest that a de-synchronized, “around-the -clock”, individual migratory movement might be present instead18,28. According to some studies, frequent shallow individual migrations could occur during summer as a result of a hunger-satiation mechanism, triggered by the abundant primary production occurring in the surface layers37. To get further insight into these aspects, first we would need to perform additional laboratory studies and field observations of krill rhythmic functions (oxygen consumption, swimming activity, DVM) under extreme photoperiods (LL-DD in the lab, summer–winter in the field). Second, laboratory and on-board trials with krill exposed to different food concentrations (low-medium–high) and different photoperiods (LD-DD-LL) could be used to study the interaction between food and light cues in the emergence of “opportunistic” rhythmic responses. More