1. bookVolume 55 (2023): Issue 1 (February 2023)
Journal Details
License
Format
Journal
eISSN
2640-396X
First Published
01 Jan 1969
Publication timeframe
1 time per year
Languages
English
Open Access

External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

Published Online: 09 Mar 2023
Volume & Issue: Volume 55 (2023) - Issue 1 (February 2023)
Page range: -
Received: 02 Aug 2022
Journal Details
License
Format
Journal
eISSN
2640-396X
First Published
01 Jan 1969
Publication timeframe
1 time per year
Languages
English

Recent investigation of host-associated microbiomes has resulted in a paradigm shift to no longer viewing animal species and their microorganisms as separate organisms, but instead as fully interdependent metaorganisms (Bosch and McFall-Ngai, 2011). Host-microbiome complexes have been studied across a wide range of animal species with significant microbial contribution to the metaorganism. Bacteria, archaea, viruses, fungi, and other microbial eukaryotes (e.g., protists) are the main components of animal microbiomes (Marchesi, 2010; Laforest-Lapointe and Arrieta, 2018). In humans, bacterial cells alone (38 trillion) outnumber human host cells (30 trillion), along with hundreds of billions of non-host eukaryotic cells (Sender et al., 2016). In addition to their sheer abundance, many microbiomes have positive effects on their animal hosts (Douglas, 2014; Hammer et al., 2019). For example, microorganisms inhabiting host guts have been shown to provide strong benefits to animal health (Peixoto et al., 2021), such as synthesizing essential amino acids in aphids (Douglas, 1996) or actively fighting infection in honey bees (Raymann and Moran, 2018). In humans, the gut microbiome has been linked to a wide range of functions, from cognitive development (Carlson et al., 2018) and behavior (Dinan et al., 2015) to dysbiosis (e.g., cancer, obesity, autism) (Thursby and Juge, 2017), illustrating the extent of possible ways microbiomes can influence their hosts.

The processes by which microorganisms assemble into communities, such as microbiomes, can be described by the ecological theory of community assembly (Nemergut et al., 2013) and can be placed into two generalized categories, defined as either stochastic or deterministic (Zhou and Ning, 2017). Stochastic/neutral processes (e.g., ecological drift, founder effects, and birth-death events) explain community composition based on random chance (Chave, 2004). By contrast, deterministic processes explain communities through niche based selection such as abiotic (e.g., pH and nutrient resources) and biotic (e.g., competition) factors (Hirzel and Le Lay, 2008) that act as selective filters. In animals with strong connections to their microbiomes, deterministic factors generated by host physical (e.g., stoma size), chemical (e.g., gut pH), or behavioral characteristics (e.g., diet preference) drive the assembly and composition of internal microbiomes (Hammer et al., 2019). Other animals with weak connections to their microbiomes often have stochastic, absent, or microbial communities similar in composition to the surrounding environment (Hammer et al., 2019).

While the animal metaorganism concept has been largely studied in the context of host internal microbiomes, external microbiomes that may be equal in importance to the metaorganism, have also been reported (Byrd et al., 2018). Like internal microbiomes, external microbiomes can directly benefit their hosts (e.g., protection against pathogens via production of antimicrobial compounds) (Chiller et al., 2001). Generally, both internal and external microbiomes consist of the same main types of microorganisms but are compositionally distinct through association with different host organs. Moreover, external microbiomes can be highly location-specific and dependent on specific features of the host body. For example, the microbiome of the human back is distinct from that of the forearm (Chiller et al., 2001; Grice and Segre 2011), as is the microbiome of the salamander’s dorsal side distinct from that of the ventral side (Bataille et al., 2016). Other animal species (e.g., finches) lack a body region distinction (i.e., microbiomes of finches’ neck and preen gland areas are all similar) (Engel et al., 2018). Compared to the strong deterministic influence of host identity in the assembly of internal microbiomes, external microbiomes are thought to be driven more by environmental factors. This is due to the constant exposure to their surroundings, making them therefore more stochastically driven in relation to the host, but this is not always the case. For example, external microbiomes of bats have been shown to be influenced by a combination of both factors (Lemieux-Labonté et al., 2016), ungulates (e.g., Artiodactyla and Perissodactyla) by host phylogeny (Ross et al., 2018), and salamanders by either factor (Prado-Irwin et al., 2017; Muletz Wolz et al., 2018; Bird et al., 2018). Overall, microbiomes are present on external host structures/organs and have a positive influence on a wide range of metazoans.

Although nematodes are the most abundant animals on Earth, remarkably little is known about their internal or external microbiomes, with a primary focus given to internal microbiomes of only a handful of nematode species (e.g., Caenorhabditis elegans) (Dirksen et al., 2016; Zhang et al., 2017). The internal bacterial microbiome of C. elegans has been shown to be primarily driven by host identity (Berg et al., 2016a; Dirksen et al., 2016) such that the same species from a wide range of geographic habitats have similar microbiomes. Moreover, populations of C. elegans retrieved from their natural environments are able to retain their wild microbiomes even when cultured on Escherichia coli (Dirksen et al., 2016), suggesting that the largest component of the bacterial community consists of the host-influenced microbiome rather than ingested bacterial food. The importance of host factors has also been observed in other nematode species. In Antarctic Dry Valley streams, for example, microbiome compositions of omnivorous Eudorylaimus antarcticus (currently under taxonomic revaluation) and bacterivorous Plectus murrayi were influenced more by the host’s identity than by environmental factors such as stream or the type of microbial mat that the nematodes were isolated from (McQueen et al., 2022). Despite evidence that the microbiomes of these two distinct nematodes were deterministically shaped by the host, this may not be true for the entire Nematoda phylum. For example, in extensive surveys of marine nematode internal microbiomes, host identity was not significant, giving credence to the role of stochasticity for some nematodes (Schuelke et al., 2018; Boscaro et al., 2022; Vafeiadou et al., 2022). Tardigrade microbiomes have been studied even less than those of nematodes, but initial studies suggest that similarly to terrestrial nematodes, they might be driven by a combination of both host-specific and environmental factors (Vecchi et al., 2018; Kaczmarek et al., 2020; Mioduchowska et al., 2021; McQueen et al., 2022; Zawierucha et al., 2022).

Although much is known about the presence of specific bacterial strains adhering to nematode cuticles (e.g., Pasteuria penetrans) (Chen and Dickson, 1998), knowledge of entire external microbiome communities is limited. These external communities may protect the nematode against pathogenic fungi or bacteria as in humans, but there is little evidence to support the presence or functional significance of external nematode microbiomes. Part of the problem lies in methods that may not distinguish between internal and external microbiomes for animals such as nematodes. External microbiomes of macrometazoans (e.g., humans, birds, and fish) have been studied by collecting samples through swabbing, scraping, or placing and then removing sticky tape (Ross et al., 2019). Similar approaches for micrometazoans such as nematodes are not feasible due to the small size of the host body (<100 μm in width) (Brady and Weil, 2014), and, consequently, the internal and external microbiomes can be confounded. For example, strong host-microbiome interactions have been observed for C. elegans and Pristionchus pacificus, but the conclusions were unable to distinguish between external-internal microbial complexes (Rae et al., 2008). Similarly, although external microbiomes of plant parasitic Meloidogyne incognita and Pratylenchus sp. nematode species have been described (Elhady et al., 2017), the inferences were based on assumptions that plant parasites lack internal microbiomes.

Another potential challenge to studying nematode external microbiomes is linked to the way nematodes are extracted from their environments such as soil. Most nematode extraction methods, including the Baermann funnel (Baermann, 1917), Whitehead tray (Whitehead and Hemming, 1965), sieving/sugar centrifugation (Jenkins, 1964), and Oostenbrink elutriation (Seinhorst, 1962), rely on the use of water to separate nematodes from soil particles. Washing over sieves (e.g., 500 μm mesh size) with excess water separates nematodes not only from soil debris, but also from microorganisms adherent to their cuticles. In fact, the process of moving nematodes and tardigrades through a series of liquid washes (e.g., sterile water or M9 buffer) has been used to remove cuticle-adherent bacteria in order to study the internal microbiome (Derycke et al., 2016; Berg et al., 2016a; Dirksen et al., 2016; Schuelke et al., 2018; Vecchi et al., 2018; Vafeiadou et al., 2022). The effectiveness of washing to remove cuticle bacteria has also been confirmed through electron microscopy (Derycke et al., 2016), smash and plate counts (Berg et al., 2016a), and fluorescence in situ hybridization (FISH) (Rivera et al., 2022).

Terrestrial nematodes live in a bacterial world, with topsoil containing over 10 million bacterial cells for every single nematode present (Chemnitz and Weigelt, 2015). Because nematodes and tardigrades continuously encounter this microbial abundance and diversity, a natural expectation would be that their cuticles contain a community of attached microbes that are yet to be discovered. Nematodes contain a non-cellular, collagen filled cuticle that is characterized by elaborate physical structures including ridges, annulations, and pores that could provide favorable space for microbial attachment. Indeed, strains of Yersinia sp. can form a biofilm on C. elegans nematodes in culture (Tan and Darby, 2004). Nematodes also actively disseminate bacteria through the soil matrix, but it is unclear if this spread occurs through defecation or from shedding of bacteria attached to the cuticle (Anderson et al., 2003). In comparison, tardigrades have 8 legs, each terminating in four to six claws, providing even more surficial space for microbes (Schill 2018; Czerneková and Vinopal 2021). Through scanning electron microscopy (SEM) and FISH, bacteria have been observed on the cuticle of nematodes and tardigrades (Bellec et al., 2018; Guidetti et al., 2019), but it is unclear if this represents a cohesive and functioning community or random microorganisms. Overall, there is a need for a natural system in which nematodes and tardigrades can be extracted in a way that allows for the potential external microbiome to be retained and studied.

The McMurdo Dry Valleys of Antarctica are the coldest, windiest, and driest deserts on Earth (Doran, 2002). The landscape is dominated by gravely unvegetated soils and ephemeral streams that flow for 8-12 weeks during austral summers, when temperatures rise high enough to melt adjacent glaciers (Fountain et al., 1999). Within the streams, there are different types of cyanobacterial mats with microbial communities that act as the habitat and food source for microinvertebrates including nematodes, tardigrades, and rotifers. Based on the dominance of specific cyanobacterial species, mats can be visually differentiated by their colors (Mcknight et al., 1999). While black mats are dominated by Nostoc and grow within stream margins, orange mats are characterized by a higher prevalence of Oscillatoriales and are typically found within the central flow of streams. Within both mat types there is a limited but well-characterized community of microinvertebrates consisting of 2 morphologically distinct nematodes (Eudorylaimus antarcticus and Plectus murrayi), 2-3 tardigrade and 15 rotifer species (Treonis et al., 1999; Porazinska et al., 2002; Adams et al., 2006). C and N stable isotope signatures have demonstrated that this simplified natural ecosystem functions at three distinct trophic levels of biotic interactions (Shaw et al., 2018). Photosynthesizing Cyanobacteria (black and orange), along with heterotrophic microbial communities, form the base of the food web. One of the nematode species, P. murrayi, as well as tardigrades and rotifers, most likely feed on bacteria and hence are thought to occupy the second (bacterial-feeding) trophic level. The second nematode species, E. antarcticus, is the sole occupant of the third and apex trophic level. Based on the C and N isotope ratios, it is thought to be the lone omnivore/predator in this system feeding on both the microbial mat communities and the other microinvertebrates (Shaw et al., 2018), although earlier studies proposed E. antarcticus to be an algivore (Wall, 2007) or an omnivore like other species of this genus (Yeates et al., 1993; Hodda, 2022). Vascular plants and aboveground macrometazoans are not present in the Dry Valleys.

The goal of our study was to analyze the external microbiome of nematodes and tardigrades by using methods that avoid the pitfalls of previously discussed extraction methods. Here, we demonstrate that nematodes and tardigrades can be isolated from microbial mats by directly picking individual specimens (e.g., with a metal or eyelash pick) from mats placed in sterile Petri dishes under a dissecting scope. Direct picking retains cuticle-adherent microorganisms that would otherwise be washed off with traditional washing and sieving steps. Although directly picked nematodes contain both external and internal microbiomes, by moving half of the specimens through a series of sterile washes, it is possible to study external microbiomes by bioinformatically subtracting sequencing reads of washed specimens from those that were unwashed. We hypothesized that nematode and tardigrade external microbiomes would be distinct from their internal microbiomes, but also from adjacent microbial mats. Furthermore, we hypothesized that due to the physical location and fewer filtering mechanisms (i.e., not passing through the stoma), external microbiome assembly would be more driven by environmental factors and hence share more similarity with mats than with internal microbiomes, which are known to be distinct from mats and host-specific.

Materials and Methods
Sample collection and DNA processing

Full site descriptions are provided elsewhere (McQueen et al., 2022), but in brief, two types of cyanobacterial mats (black and orange) were collected during 18-21 January 2019 from four seasonally active streams (Canada, Bowles Creek, Delta, Von Guerard) in Taylor Valley, Antarctica. For each stream, one representative plot with both mat types (2 m in radius) was randomly selected, and three replicates of each mat type without any sediments (7 cm x 7 cm) were collected with a sterile scalpel for a total of 24 mat samples (12 black and 12 orange). Samples were frozen, kept in the dark, transported to the University of Florida, and stored at -20°C. Approximately 30 g of mat samples were slowly defrosted (10°C every 24 hours) to 4°C and examined for microinvertebrates in sterile Petri dishes under a dissecting microscope. No decanting, sieving, or disturbance of any kind was performed to keep all possible adherent external microbiomes intact. Single specimens visually identified as alive E. antarcticus (n=128) (currently undergoing taxonomic revision), P. murrayi (n=345), and Tardigrada (n=169) were manually picked from mats directly with a metal pick, and approximately half were vigorously moved through three washes of cold sterile water (E. antarcticus n=88, P. murrayi n=176, Tardigrada n=94), while the other half remained unwashed (E. antarcticus n=40, P. murrayi n=169, Tardigrada n=75). To validate the effectiveness of washing in the removal of external microorganisms, we compared washed and unwashed specimens using SEM at the University of Florida Interdisciplinary Center for Biotechnology Research (details below). Following isolation, single nematode or tardigrade specimens were placed in individual microcentrifuge tubes and DNA extracted using 25 μL of a proteinase K lysis buffer (McQueen et al., 2022). Substrate mat DNA was extracted from ~0.3 g of mat slurry with a Qiagen DNeasy PowerSoil Kit. Although extracted mat DNA could theoretically also contain DNA from microinvertebrates and their microbiomes, due to their general low abundance in mats, the likelihood of recovering even a single individual in the ~0.3 g was very low. High throughput metabarcoding was used to characterize bacterial and eukaryotic microbial communities with 16S (515F/926R) and 18S (1391f/EukBr) rRNA gene markers (Caporaso et al., 2012) following the Earth Microbiome Project protocols (https://earthmicrobiome.org/) To confirm successful amplification, all PCR-ed samples and technical replicates were visualized using gel electrophoresis (1.5% agarose). Three PCR replicates were pooled and sent to the Hubbard Center for Genome Studies, University of New Hampshire, along with PCR and lysis buffer negative controls for barcode attachment, library preparation, and paired-end sequencing on an Illumina HiSeq 2500 (2x250bp) (Illumina Inc., CA, USA). Due to the limited number of available barcodes, samples were split among four separate sequencing runs. To examine potential variation among the runs, DNA from the same 16 samples were sequenced on two of the four runs.

Bioinformatics and Construction of External Microbiomes

Following read demultiplexing by the sequencing facility, reads were imported into QIIME2 (Bolyen et al., 2019) and primers were removed with cutadapt (Martin, 2011). Initial data screening included trimming reads of nucleotides at < 30 quality score, as well as unpaired forward 16S reads (196 bp) and paired 18S reads (average 131 bp) being de-novo clustered to 100% similarity amplicon sequence variants (ASVs) using DADA2 (Callahan et al., 2016). Filtering criteria during the DADA2 processing included input quality filtering, chimera removal, and an algorithmic error model of the sequencing instrument. Taxonomy was assigned using the assign_taxonomy.py script in QIIME1.9 (Caporaso et al., 2010) against the SILVA 138 database for 16S reads and the SILVA 111 database for 18S reads (Quast et al., 2013), both with any “uncultured” reference sequences removed. From the constructed table, non-bacterial sequences in the 16S ASV table and non-eukaryotic sequences in the 18S ASV table were removed. In addition, 18S sequences with hits to a specific microinvertebrate host were removed only from that host’s microbial community, as were ASVs with poor assignments (<90% of query coverage or <95% ID) removed from all hosts. If present, any of the few sequences identified in negative controls were subtracted from experimental samples. Reads assigned to Phormidesmis were changed to Phormidium due to the uncertain nature of this cyanobacterial clade (Komárek et al., 2009; Thomazeau et al., 2010; Raabová et al., 2019). Based on ASV rarefaction curves indicating insufficient sequencing depth to recover the full diversity, 184 samples with less than 100 total 16S reads or 18S reads were discarded.

Across all samples, the total number of unwashed individuals of E. antarcticus, P. murrayi, and Tardigrada were 32, 103, and 72, respectively; the total number of washed individuals of E. antarcticus, P. murrayi, and Tardigrada were 52, 110, and 89, respectively. For each washed microinvertebrate individual, one representative internal microbiome (gut microbiome) was calculated by first removing all host ASVs and then averaging all remaining individuals for each of the 24 mat replicates. External microbiomes of all microinvertebrates were derived bioinformatically (Fig. 1) using the following steps. First, host ASVs were removed from all unwashed (and thus containing both external and internal microbiomes) individuals of microinvertebrates, and remaining ASVs were averaged to create one unwashed microbiome per each mat replicate. Second, to derive representative external microbiomes, ASVs (based on absolute abundance) of internal microbiomes of washed individuals were subtracted from the corresponding (i.e., assigning to the same ASV) ASVs of unwashed microbiomes for each mat replicate (Fig. 1). Although the number of reads within unwashed ASVs were generally higher than as those in the washed group, any subtractions resulting in negative values (i.e., reads were present in washed but not unwashed microbiomes) were equalized to zero. Although we refer to “microbiome” as the entire detected microbial community, we acknowledge that this may represent not only resident microorganisms but also non-viable eDNA.

Figure 1

Graphical abstract of methods used to construct external and internal microbiomes of nematodes and tardigrades. For internal microbiomes, microinvertebrates were washed, sequenced, and host sequences subtracted before being averaged for each of the 24 mat replicates and 3 host types. For external microbiomes, unwashed microinvertebrates were sequenced, host sequences subtracted, and averaged for each of the 24 mat replicates and host types. The internal ASV abundances were then subtracted from the corresponding ASVs of unwashed community of the same mat replicate and microinvertebrate type to create the final external microbiome.

Statistics and Visualization

Statistics were performed in R Version 3.6.1 (R Core Team, 2020). Prior to any analyses, all ASV read counts were converted to relative abundance. Alpha diversity metrics (i.e., ASV Richness, Simpson’s, Shannon’s, and Faith’s Phylogenetic Diversity) were calculated with Hill Numbers using hill_taxa() from the hillR package (Alberdi and Gilbert, 2019). A linear model was run to compare alpha diversity of mat communities to microinvertebrates with the lm() function from the base R environment. For testing alpha diversity among microinvertebrate microbiomes, a linear mixed effects model was conducted with the lmer() function using Kenward-Roger degrees of freedom from the lme4 package (Bates et al., 2015). Microinvertebrate host (E. antarcticus vs. P. murrayi vs. Tardigrada), microbiome type (external vs. internal), mat type (black vs. orange), and stream (Canada vs. Bowles Creek vs. Delta vs. Von Guerard) were tested as fixed factors with microinvertebrate nested under mat replicate as a random factor. Post hoc significance to compare groups was determined via the $contrasts output of emeans() from the emmeans package (Searle et al., 1980). Compositional differences among microbiomes based on Bray Curtis dissimilarity matrices were tested with the vegan 2.5.7 package (Dixon 2003; Oksanen et al., 2019) through permutational analysis of variance (PERMANOVA) with 9999 permutations using the adonis() function. Three PERMANOVA tests were run; the first was to test the influence of microinvertebrate (E. antarcticus vs. P. murrayi vs. Tardigrada), microbiome type (external vs. internal), mat type (black vs. orange), and stream (Canada vs. Bowles Creek vs. Delta vs. Von Guerard) (independent variables) on all external and internal microbiome communities (dependent variables) together. The second was to test external microbiomes only (for the same above factors but excluding microbiome type), and the third was to test internal microbiomes only. Pair-wise, post hoc contrasts of any ordinated group centroids were calculated using the pairwise.adonis2() function within the pairwiseAdonis package (Martinez Arbizu, 2022). Bray Curtis dissimilarity matrices were also tested with the betadisper() function in vegan to analyze the multivariate homogeneity of group dispersions between each community. Values of dispersion were tested using the same linear mixed effects model as for microinvertebrate microbiomes alpha diversity metrics. Relative abundance of selected taxa (e.g., at phylum, family, and genus levels) was tested using a linear mixed effects model as described above. All figures were created in R using ggplot2 (Wickham, 2016). Raw reads are available at the NCBI Sequence Read Archive with the project ID PRJNA851105. Documented code for the full bioinformatic pipeline, figure creation, and statistical analysis is available at www.WormsEtAl.com/externalmicrobiomes and https://github.com/WormsEtAl/ExternalMicrobiomes.The 16 duplicate samples sequenced on two different sequencing runs showed no differences in alpha diversity and community composition. In addition, the presented results below reflect community diversity and composition based on the relative abundance of microbial reads, and not of their absolute cell counts.

Scanning Electron Imaging

To illustrate the effectiveness of the external microbiome removal via washing, ~60 additional specimens of E. antarcticus were picked from black mats from all four streams. Half of the individuals were passed through three washes of cold sterile water, while the other half remained unwashed. All specimens were then picked into a 4% glutaraldehyde fixative and transported to the ICBR where they were dehydrated with 2% osmium tetroxide and followed with EtOH replacement for critical point drying (Eisenback, 1986). Fixed and dried specimens were mounted on SEM stubs and sputter-coated with gold overnight. Samples were then imaged using a Hitachi SU5000 Schottky Field-Emission Variable Pressure microscope.

Results
Alpha Diversity Differences among Communities

All host bacterial microbiomes (external and internal) were significantly less diverse than mat microbiomes regardless of the alpha diversity metrics used (P<0.003, SI Table 1, SI Table 3). While neither mat type nor stream influenced alpha diversity of host microbiomes, (P>0.12, SI Table 2A), host ID and microbiome type were important factors (P<0.01). Specifically, microbiomes of Tardigrada were the most diverse (Richness and Faith’s PD), followed by P. murrayi, and E. antarcticus was the least diverse (Fig. 2A, SI Table 3). External microbiomes were more diverse (Shannon’s, Simpsons, and Faith’s PD) than the internal microbiomes (SI Table 2A), and trends were similar in direction and magnitude for all animal hosts (Fig. 2A, SI Table 2A, 3A). The difference between the two microbiome types (i.e., external vs internal) was lowest for E. antarcticus, followed by P. murrayi, and was highest for Tardigrada. In addition, the extent of variation (standard deviation) was higher for external than internal microbiomes across all microinvertebrate hosts (Fig. 2, SI Table 3A).

Figure 2

Diversity of microinvertebrate bacterial external and bacterial internal microbiomes. (A) Shannon’s diversity (box plot using Hill Numbers) with a significant difference between microbiomes (P=0.03, GLM) but not microinvertebrates (P=0.14), streams (P=0.22), or mat types (P=0.52). (B) Compositional difference based on Bray Curtis distance matrix visualized with a NMDS ordination, in which microinvertebrate host explained the most variation (R2 =0.14, PERMANOVA). Stars show centroid location of microbiome type, and solid circles show individual microbiomes.

Similar to bacterial microbiomes, all host-associated eukaryotic microbiomes were less diverse than mat communities (SI Table 1). However, in contrast to bacterial microbiomes, there were no differences in diversity metrics between eukaryotic external and internal communities (SI Table 2B). There was also no effect of mat type nor stream (SI Table 3B), but external eukaryotic communities were significantly affected by the microinvertebrate host where the most diverse microbiomes were those of Tardigrada, followed by P. murrayi and then E. antarcticus.

Compositional Differences among Communities

The composition of microinvertebrate bacterial communities was significantly affected by all tested factors including microinvertebrate host, microbiome type, mat type, and stream (PERMANOVA, P<0.01, Table 1A); however, the amount of variation explained by these factors varied greatly. Microinvertebrate host was the most important factor, explaining 14% of internal and external bacterial community variation (Table 1A), and separating microinvertebrate microbiomes into three distinct clusters in the NMDS space (Fig. 2B). Other factors were less significant, with stream explaining 6%, microbiome type 2%, and mat type 1% (Table 1A). Dispersion analysis indicated that both microbiome type (P=0.03) and stream (P<0.01) were significant, but not host (P=0.44) or mat type (P=0.64). Within microbiome type, specifically the variation among external bacterial microbiomes of P. murrayi and Tardigrada (but not of E. antarcticus) was significantly higher (P<0.03) than among internal microbiomes (SI Fig. 1). To better understand the contribution of specific factors driving each microbiome type, we subsequently examined their compositions separately (Table 1B,C). The composition of internal microbiomes was primarily influenced by microinvertebrate host (23% of variation) and to a lesser degree stream (7%), but not by mat type (P=0.23) (Table 1C). On the other hand, external microbiome composition was influenced by a combination of stream (10%) and microinvertebrate host (8%), followed by mat type (3%) (Table 1B). Splitting the models by microbiome types showed that while all factors tested were significant in explaining community composition, microinvertebrate host was the best predictor of internal microbiomes, while external microbiomes were more impacted by the environment, as stream explained the most variation in those cases.

Differences in bacterial community composition of A. all microbiomes, B. external microbiomes, and C. internal microbiomes using a PERMANOVA.

A. External and Internal Microbiomes
B. External Microbiomes
C. Internal Microbiomes
PR2PR2PR2
Host< 0.000.14< 0.000.08< 0.000.23
Microbiome< 0.000.02n.a.n.a.n.a.n.a.
Mat< 0.000.010.020.030.230.01
Stream< 0.000.06< 0.000.10< 0.000.07
Host:Microbiome< 0.000.03n.a.n.a.n.a.n.a.
Host:Mat0.090.020.110.050.480.02
Microbiome:Mat0.79< 0.00n.a.n.a.n.a.n.a.
Host:Microbiome:Mat0.470.01n.a.n.a.n.a.n.a.

Comparisons included microinvertebrate host (E. antarcticus or P. murrayi or tardigrades), microbiome type (external or internal), mat type (black or orange), stream (Canada or Bowles Creek or Delta or Von Guerard), and their interactions. The abbreviation “n.a.” is used to depict when a term was not included in the model. Factors explaining the largest variation (R2) of each model are underlined and statistically significant differences are highlighted in bold.

Despite significant differences in external and internal bacterial microbiomes (Table 1A), they largely overlapped in NDMS space (Fig. 2B), although the external microbiomes of all three microinvertebrates shifted to ordinate closer to mats than their internal microbiomes, as evident from the analysis of centroids (SI Fig. 2). This potentially higher mat-external microbiome similarity was further assessed with pairwise post hoc contrasts, showing that all microbiome types (external and internal of all animal types) were statistically distinct from mat microbiomes (P<0.002). However, the R2 values generated from pairwise contrasts comparing mats to external microbiomes (0.09 for E. antarcticus, 0.11 for P. murrayi, and 0.14 for Tardigrada) were uniformly lower than the R2 values comparing mats to internal communities (0.14 for E. antarcticus, 0.24 for P. murrayi, and 0.23 for Tardigrada), resulting in greater mat-external microbiome similarity compared to mat-internal microbiome differences.

The relative abundance of bacterial taxa within external and internal microbiomes differed across microinvertebrates (Fig. 3A-D, SI Table 5). All microbiomes were primarily comprised of Cyanobacteria, Proteobacteria, and Bacteroidota, totaling 87.0% of entire bacterial communities. In comparison to mats, both microbiome types were depleted in Cyanobacteria, however, more Cyanobacteria were present within the external microbiomes than in the internal microbiomes of all microinvertebrate hosts (P=0.01, Fig. 3A). Tychonema was the most abundant cyanobacterial genus within external and internal microbiomes of all microinvertebrates, comprising on average 9.5% and 3.0%, respectively. In contrast to the uniform trends of Cyanobacteria, patterns for Bacteroidota were more host-specific. For example, the relative abundance of Bacteroidota within external microbiomes of E. antarcticus was 66% higher than internal microbiomes (P< 0.01, Fig. 3B). This pattern was the opposite for P. murrayi and Tardigrada (P<0.001 and P=0.13, respectively). While Tychonema was the single dominant taxon within Cyanobacteria for all microinvertebrates, genera driving the overall abundance of Bacteroidota differed among microinvertebrates. Flavobacterium only dominated external microbiomes of E. antarcticus and was significantly more abundant compared to any other microbiome (P=0.01, pairwise post hoc contrasts). Both microbiomes of P. murrayi were dominated by Larkinella. Finally, both Tardigrada microbiomes were equally enriched in similar genera including Ferruginibacter and Chryseobacterium. Although there were no differences in the abundance of Proteobacteria between microbiome types (external or internal) (P=0.13, Fig. 3C) across all microinvertebrate hosts, external microbiomes of E. antarcticus were significantly more enriched in Proteobacteria compared to internal E. antarcticus microbiomes (P=0.01). Among Proteobacterial taxa, all microbiomes were equally dominated by the cryophilic Polaromonas in the family of Comamonadaceae (Fig. 3C). Except for the single genus Pseudomonas (in the family of Pseudomonadaceae) with significantly higher relative abundance within internal microbiomes of E. antarcticus, the abundance of other Proteobacterial taxa was mostly similar. The patterns for taxa within less abundant phyla (e.g., Actinobacteriota) were also highly host- and microbiome type-specific (Fig. 3D).

Figure 3

Relative abundance of bacterial communities of microinvertebrate external and internal microbiomes: (A) genera of Cyanobacteria, (B) genera of Bacteroidota, (C) families of Proteobacteria, (D) genera of Actinobacteriota.

Although microinvertebrate host, microbiome type, and stream were significant for eukaryotic community composition, microbiomes did not visibly cluster by any factor (SI Fig. 3). The amount of community composition variation explained by each factor was substantially and consistently lower than for bacterial microbiomes (5% vs. 14%, 1.6% vs. 2.0%, and 3.4% vs. 6%, respectively) (Table 1A, SI Table 4A). Consequently, more community variation remained unexplained (79.6%), compared to bacterial communities (69%). When examining only external microbiomes, eukaryotes were equally explained by the host and stream (9%) (SI Table 4B). For only internal eukaryotic microbiomes, host explained the same amount of variation (9%), and the amount explained by stream lowered to 6% (SI Table 4C). Fungi made a significant contribution to all microbiomes (Figure 4A, SI Table 6) and its abundance significantly differed between microinvertebrate hosts (P<0.001), but not between microbiome type (P = 0.14, SI Table 5). Fungi (e.g., Ascomycota and Basidiomycota) were enriched in internal microbiomes of P. murrayi and Tardigrada, compared to all other communities with pairwise post hoc tests (P<0.0001, Fig. 4C). Generally, external microbiomes of nematode species were dominated by non-host metazoans, E. antarcticus by tardigrades, and P. murrayi by rotifers. In addition, internal microbiomes of E. antarcticus were characterized by the largest relative abundance (SI Table 6) of non-host metazoans including tardigrades and rotifers but no P. murrayi (Fig. 4B). No E. antarcticus was recovered in external microbiomes of P. murrayi, and no P. murrayi was in the external microbiomes of E. antarcticus.

Figure 4

Relative abundance of eukaryotic communities of microinvertebrate external and internal microbiomes: (A) total non-host community (B) metazoan phyla, (C) fungal clades.

Verification of Washing Methods

Specimens of washed and unwashed nematodes of both species were examined under SEM. Compared to the 9 washed E. antarcticus examined, all 6 unwashed individuals contained more adherent material along the entire length of the nematode body. Although washed nematodes did retain a small amount of this material, the great majority was consistently removed from all 9 individuals examined. Representative photos comparing different body regions of unwashed and washed individuals of E. antarcticus are provided in supplementary material (SI Fig. 4A-4J). Attached material was concentrated around nematode anatomical features such as the head (SI Fig. 4A,B), between annules and lateral lines (SI Fig. 4C,D), and around excretory pores (SI Fig. 4E). Organic material was most concentrated along the cervical region where the lips meet the body, as well as the stomatal opening. Sizes of organic materials varied considerably (0.05-1.3 μM) and likely consisted primarily of bacterial biofilm (SI Fig. 4G), clumps of mat fibers, and fungal hyphae (SI Fig. 4A). Single bacterial cells were also observed, but most were found under the possible biofilm-like layer (SI Fig. 4H). Interestingly, we routinely observed an off-axis line (i.e., not aligned to annulations or lateral field) of material in many of the unwashed specimens (SI Fig. 4I). Although remnants of this line were also present in the washed specimens, it was always less apparent (SI Fig. 4J). Overall, SEM demonstrated the effectiveness of nematode washing in removing the majority of potential external microbiomes.

Discussion

Many animal species contain a collection of microorganisms that live in direct association with the host. This combined metaorganism has often been studied in the context of gut microbiomes, showing that microbiota provide direct functional benefits to nematodes (e.g., C. elegans) (Clark and Hodgkin, 2014; Berg et al., 2016b), and to many but not all metazoans (Hammer et al., 2019). Community assembly of a select few other nematode and tardigrade internal microbiomes have been described (Derycke et al., 2016; Berg et al., 2016a; Dirksen et al., 2016; Schuelke et al., 2018; Vecchi et al., 2018; McQueen et al., 2022; Vafeiadou et al., 2022), showing strong host influences on the internal microbial communities of terrestrial nematodes, which suggests a symbiotic importance as has been found in other hosts with deterministic microbiomes. In addition to gut microbiomes, host-associated microbiomes can also colonize the skin, and these external microbiomes likely hold equal importance to the host (Byrd et al., 2018; Ross et al., 2019). For nematodes and tardigrades, single strains of bacteria are known to adhere to the cuticle, but the community composition of the external microbial community has not been previously described.

Using nematodes and tardigrades from Antarctic cyanobacterial mats, we were able to computationally construct and describe the presence and composition of external microbiomes by comparing washed and unwashed specimens. For all microinvertebrate species (E. antarcticus, P. murrayi, and Tardigrada), external bacterial microbiomes were more diverse than their respective internal microbiomes, but less diverse than the surrounding microbial mats they inhabit and feed on, supporting our hypothesis that nematodes and tardigrades have diverse external microbiomes. This gradient of reduced diversity indicates greater deterministic selective pressure on internal microbiomes due to them potentially having more ecological “filters” (e.g., physical size limitation of the stoma aperture, behavioral feeding habits) and other unexplored factors influencing the community assembly (e.g., pH, feeding maceration) than external microbiomes. Due to the physical location of the external microbiome and its greater proximity to the environment, it would be expected that its composition would be less influenced by the host, with a greater influence of the environment. However, we found that bacterial composition of all host microbiomes was distinct from mats and microbiomes ordinated by host type with a complete overlap of external and internal microbiomes for each host. This indicates a larger role of the host on the external microbiome than we anticipated. Despite this, R2 values generated from pairwise contrasts comparing mats to external communities were lower for each host than R2 values comparing mats to respective internal communities. The lower explained variation indicates more overlap and similarity of the mats to the external microbiomes than of mats to internal microbiomes. Furthermore, a combined PERMANOVA model showed that although microbiome type (external or internal) was significant in explaining community composition, microinvertebrate host and stream were both more important in explaining community composition. Using the split model to examine external and internal microbiomes separately, the importance of environmental and host factors swapped, with external microbiomes being more explained by stream, while internal microbiomes were more explained by the microinvertebrate host, highlighting the larger influence the environment has on external microbiomes. This supports the first part of our hypothesis in that external microbiomes are distinct from mats and among microinvertebrates, but contrasts the second half, as there was a smaller difference between the composition of microbiome types (external or internal) than expected. Overall, external and internal microbiomes of a host were more similar to each other than they were different, with only 2% of variation explained by the microbiome type. However, our PERMANOVA was able to discern a greater influence of environmental factors for external microbiomes compared to internal microbiomes with external microbiomes more resembling mats. This can also be seen in relative abundance of communities, as external microbiomes of all animal types contained a higher relative abundance of Cyanobacteria than internal microbiomes, as they live in a substrate of cyanobacterial mats. This similarity of taxa between microbiomes, but reduction of diversity in the internal microbiome, may indicate that the cuticle provides a primary level of filtering for the assembly of the internal bacterial microbiome and acts as an important deterministic factor for the overall host-microbiome complex.

Although non-host eukaryotic microbiomes were less diverse than the eukaryotic community of the mats, there was no difference in the diversity between microbiome types (external or internal), nor did communities cluster by host or microbiome type. Instead, these communities appeared to follow a more stochastic assembly, which is reflected by the eukaryotic PERMANOVA model explaining 10.1% less of the community variation than for the bacterial communities. External and internal eukaryotic microbiomes were of the same diversity, and consistent patterns of taxa abundance were observed. There was also little similarity among shared feeding groups (i.e., P. murrayi and Tardigrada) or host phylum (i.e., E. antarcticus and P. murrayi). Although both nematode internal microbiomes were enriched in fungi compared to the external, these were comprised of distinct fungal communities (dominated by Ascomycota or Basidiomycota, respectively). In addition, the external microbiomes of both nematodes were dominated by non-host metazoans (e.g., rotifers and tardigrades), but this was not the case for the Tardigrada hosts. Unlike for fungi or bacteria, there is no SEM evidence suggesting that non-host metazoans have colonized the cuticle of nematodes and/or tardigrades, so instead these reads may originate from free-floating eDNA and indicate possible biotic interactions. E. antarcticus is well known as the sole omnivore/predator in the system (Shaw et al., 2018), so it is surprising that the external microbiome of P. murrayi contained similar levels of non-host metazoans. The dominance of non-host metazoans in nematode external microbiomes is even more intriguing given that we did observe ample fungi in SEM images, but sequencing data indicates ample non-host metazoan DNA. This opens the possibility for tardigrades and rotifers to be more physically connected to nematodes in day-to-day behavior and functions than previously thought.

External microbiomes hold the potential to act as a local species pool for internal gut microbiomes. Although nematode and tardigrade cuticles are impermeable to bacteria (Bird and Bird, 1991; Schill, 2018), transfer through the stoma is possible even for non-bacterial-feeding specialists including omnivorous nematodes. In our SEM imagery, we observed a greater concentration of organic matter on the lips of nematodes, even for the omnivorous E. antarcticus. Bacterivorous nematodes may actively feed by selecting specific bacterial species/strains for sustenance, but fortuitously draw in other bacteria previously attached to their own lips. We propose that the cuticle acts as an ecological filter to narrow down the regional pool that is further filtered to become the internal microbiome (the community most distinct from the mats). In support of this hypothesis, we found that the external microbiomes of all animal types were more diverse than their internal microbiomes, and that external and internal communities contained similar host-specific taxa. For example, Larkinella was found both in high concentrations of P. murrayi external and internal microbiomes, but not in other hosts. If Larkinella can colonize the P. murrayi cuticle, the internal microbiomes consequently might also contain high concentrations of Larkinella due to the constant exposure. In addition to indirect influences on assembly of what can attach to the cuticle, there may be direct host physiology influencing the external microbiome. In humans, for example, bacteria within the gut directly influences the composition of the skin microbiome (O’Neill et al., 2016), through the production and release of metabolites (Salem et al., 2018). Our results show a surprisingly large influence of host identity on external microbiomes, despite all hosts residing in the same environment. As the two nematodes are comprised of similar cuticle physical structure, there may be other host-specific interactions occurring. As part of the secretory and excretory system, pores exist in the cuticle of nematodes (Bird and Bird, 1991) and tardigrades (Schill, 2018; Czerneková and Vinopal, 2021) that produce secretions and other organic molecules. The functions of these compounds are mostly unknown, but in nematodes, these secretions produce a surface coat (i.e., glycocalyx) that is involved in protection against fungal pathogens (Blaxter et al., 1992). If nematodes secretions are actively mediating the attachment of bacteria to the cuticle, this could influence gut microbiome composition and vice versa.

As part of a normal lifecycle, nematodes move both vertically and horizontally throughout the soil matrix in search for food. Although soil matrix obscures observations of this movement, in food-depleted cultures, nematodes release signaling chemicals to activate movement around the Petri dish (Harvey, 2009; Kaplan et al., 2012). In addition to active transport, nematodes can be transported passively at local scales (meters) in the environment by earthworms and other meiofauna (Shapiro et al., 1993) and at regional scales by macrofauna (e.g., birds and large mammals), the wind, or water (Nkem et al., 2006; Frisch et al., 2007; Vanschoenwinkel et al., 2008; Ptatscheck et al., 2018). Regardless of the scale and transport, nematodes measurably contribute to the belowground ecosystem by spreading bacteria at a greater distance than any motile bacterium can by itself (Yang and van Elsas, 2018). While it remains unclear if this nematode-mediated spread is a result of defecation from the digestive tract or from external microorganisms detaching from the nematode cuticle (Anderson et al., 2003), by establishing the presence of diverse and distinct microbiomes, our study indicates that both scenarios could be equally important.

Although we were able to describe computationally generated external microbiomes, technical limitations currently prevent the direct analysis of external microbiomes. Due to the small body size of nematodes and tardigrades, we were unable to physically separate or sample only the external surface of the hosts. The major limitation of this issue is that we could not evaluate external microbiomes at the level of a single individual. Another inherent issue with the methods used for this study is the inability to distinguish potential cuticular microbiome differences associated with specific body locations as in other animals such as humans or salamanders (Chiller et al., 2001; Grice and Segre, 2011; Bataille et al., 2016). Because nematode cuticles vary along the nematode body, it could be expected that the microbiomes also vary. From our SEM images, we observed a greater concentration of organic matter around specific cuticular features (e.g., SE and amphidial pores, neck, and annulations), suggesting increased microbial abundance at these sites. Although the cuticles of E. antarcticus and P. murrayi are both annulated, cuticles of P. murrayi are well defined compared to the finely annulated E. antarcticus (Yeates, 1970). Consequently, gaps between annules of P. murrayi are wider and deeper and provide more space for attachment of external material than of E. antarcticus. In addition, caudal glands, exiting through a spinneret at the posterior end in P. murrayi, provide another inhabitable space for the external microbiome, all of which are absent in E. antarcticus (Yeates, 1970). Tardigrade cuticles contain larger folds along the body and legs, potentially trapping even more material (Schill, 2018). These physical body differences could explain the difference between external and internal microbiome diversity: the smallest for E. antarcticus, followed by P. murrayi and the largest for Tardigrada, but this could have been confounded with a similar pattern of internal diversity. Accounting for differences in raw diversity, dispersion analysis of communities showed that the external microbiomes of P. murrayi and Tardigrada were more variable than respective internal microbiomes, in contrast to the external and internal microbiomes of E. antarcticus, which were equally variable. Another limitation is the possibility of retaining adherent microorganisms in our internal microbiomes despite our washing steps. Although our SEM photos show most external microorganisms were removed, some did remain. More invasive washing steps, such as a bleach solution, are conceivable, but in practice are likely to damage the DNA of associated microorganisms including the internal microbiome. Although our insights focus on relative abundance, diversity, and composition, we are unable to evaluate the absolute abundance of microorganisms. More quantitative methods such as cell count and qPCR could be used in future studies to more precisely evaluate differences of abundance between communities. Overall, despite prevailing limitations, we were able to provide the most accurate representation of nematode and tardigrade external microbiomes to date.

In general, our study provides evidence that nematodes and tardigrades contain a robust bacterial external microbiome that is greater in diversity than the host’s internal microbiome. In addition, external bacterial microbiome composition was more influenced by environmental factors (i.e., stream, mat type) than internal microbiomes were, but both were most dependent on undescribed deterministic host factors. Eukaryotic external microbiomes appear to be less developed, with more stochastic patterns present. This could indicate a functional role of external bacterial microbiomes to the host, as animals who do not need a sustained external microbiome would not have evolved the behavioral or physical properties to influence that community.

Figure 1

Graphical abstract of methods used to construct external and internal microbiomes of nematodes and tardigrades. For internal microbiomes, microinvertebrates were washed, sequenced, and host sequences subtracted before being averaged for each of the 24 mat replicates and 3 host types. For external microbiomes, unwashed microinvertebrates were sequenced, host sequences subtracted, and averaged for each of the 24 mat replicates and host types. The internal ASV abundances were then subtracted from the corresponding ASVs of unwashed community of the same mat replicate and microinvertebrate type to create the final external microbiome.
Graphical abstract of methods used to construct external and internal microbiomes of nematodes and tardigrades. For internal microbiomes, microinvertebrates were washed, sequenced, and host sequences subtracted before being averaged for each of the 24 mat replicates and 3 host types. For external microbiomes, unwashed microinvertebrates were sequenced, host sequences subtracted, and averaged for each of the 24 mat replicates and host types. The internal ASV abundances were then subtracted from the corresponding ASVs of unwashed community of the same mat replicate and microinvertebrate type to create the final external microbiome.

Figure 2

Diversity of microinvertebrate bacterial external and bacterial internal microbiomes. (A) Shannon’s diversity (box plot using Hill Numbers) with a significant difference between microbiomes (P=0.03, GLM) but not microinvertebrates (P=0.14), streams (P=0.22), or mat types (P=0.52). (B) Compositional difference based on Bray Curtis distance matrix visualized with a NMDS ordination, in which microinvertebrate host explained the most variation (R2 =0.14, PERMANOVA). Stars show centroid location of microbiome type, and solid circles show individual microbiomes.
Diversity of microinvertebrate bacterial external and bacterial internal microbiomes. (A) Shannon’s diversity (box plot using Hill Numbers) with a significant difference between microbiomes (P=0.03, GLM) but not microinvertebrates (P=0.14), streams (P=0.22), or mat types (P=0.52). (B) Compositional difference based on Bray Curtis distance matrix visualized with a NMDS ordination, in which microinvertebrate host explained the most variation (R2 =0.14, PERMANOVA). Stars show centroid location of microbiome type, and solid circles show individual microbiomes.

Figure 3

Relative abundance of bacterial communities of microinvertebrate external and internal microbiomes: (A) genera of Cyanobacteria, (B) genera of Bacteroidota, (C) families of Proteobacteria, (D) genera of Actinobacteriota.
Relative abundance of bacterial communities of microinvertebrate external and internal microbiomes: (A) genera of Cyanobacteria, (B) genera of Bacteroidota, (C) families of Proteobacteria, (D) genera of Actinobacteriota.

Figure 4

Relative abundance of eukaryotic communities of microinvertebrate external and internal microbiomes: (A) total non-host community (B) metazoan phyla, (C) fungal clades.
Relative abundance of eukaryotic communities of microinvertebrate external and internal microbiomes: (A) total non-host community (B) metazoan phyla, (C) fungal clades.

SI Figure 1

Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.

SI Figure 2

Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.

SI Figure 3

Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.

SI Figure 4

SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.
SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.

Means and standard error of the mean (SE) for relative abundance of selected taxa (Cyanobacteria, Bacteroidota, Proteobacteria, Actinobacteriota, Flavobacterium, Larkinella, Fungi, Metazoa), within external and internal microbiomes. Abundances are reported as a proportion of the entire community (i.e., 0-1).

Cyanobacteria Bacteroidota Proteobacteria Actinobacteriota
Proportion SE Proportion SE Proportion SE Proportion SE
E. antarcticus External 0.03 0.01 0.75 0.06 0.19 0.08 0.00 0.01
Internal 0.02 0.01 0.09 0.08 0.81 0.10 0.01 0.00
P. murrayi External 0.22 0.06 0.37 0.05 0.29 0.03 0.02 0.01
Internal 0.06 0.02 0.64 0.06 0.20 0.04 0.03 0.01
Tardigrada External 0.04 0.03 0.47 0.07 0.33 0.05 0.01 0.00
Internal 0.01 0.02 0.62 0.05 0.29 0.03 0.01 0.00
Flavobacterium Larkinella Fungi Metazoans
Proportion SE Proportion SE Proportion SE Proportion SE
E. antarcticus External 0.72 0.05 0.00 0.00 0.10 0.03 0.64 0.13
Internal 0.02 0.01 0.00 0.00 0.16 0.07 0.17 0.08
P. murrayi External 0.06 0.01 0.21 0.05 0.53 0.07 0.37 0.06
Internal 0.02 0.00 0.51 0.06 0.86 0.04 0.04 0.01
Tardigrada External 0.07 0.03 0.00 0.00 0.37 0.09 0.01 0.00
Internal 0.13 0.04 0.00 0.00 0.74 0.04 0.04 0.02

Means and standard error of the mean (SE) of alpha diversity metrics (Shannon’s, Simpsons, Richness, Faith’s PD) for A. bacterial and B. eukaryotic external and internal microbiomes of three hosts (E. antarcticus or P. murrayi or Tardigrada). Alpha diversity metrics were calculated with Hill Numbers.

A. Bacterial Diversity Shannon’s Index Simpsons Index Richness Faith’s PD
Mean SE Mean SE Mean SE Mean SE
E. antarcticus External 18.60 7.69 10.36 4.38 43.5 16.08 5.20 1.37
Internal 12.46 5.76 8.35 3.83 24.92 9.51 3.47 0.99
P. murrayi External 35.86 7.63 16.23 4.32 161.14 26.84 12.24 1.40
Internal 22.25 7.38 9.21 2.71 114.09 25.58 9.75 1.39
Tardigrada External 43.64 8.14 16.21 3.21 446.20 85.27 21.52 2.96
Internal 22.64 4.96 8.99 1.70 297.30 56.03 15.84 2.12
B. Eukaryotic Diversity Shannon’s Index Simpsons Index Richness Faith’s PD
Mean SE Mean SE Mean SE Mean SE
E. antarcticus External 6.07 1.25 3.34 0.77 26.75 6.70 5.06 0.84
Internal 6.92 1.22 3.97 0.73 21.29 4.47 3.67 0.60
P. murrayi External 11.18 1.57 8.82 1.40 16.80 2.91 3.17 0.46
Internal 13.34 1.65 10.76 1.36 18.77 2.33 3.53 0.31
Tardigrada External 17.51 4.62 13.08 3.81 34.33 7.12 5.41 0.83
Internal 18.82 3.81 14.67 2.75 26.29 5.80 4.67 0.78

Differences in eukaryotic community composition of A. all microbiomes, B. external microbiomes, and C. internal microbiomes using a PERMANOVA. Comparisons included microinvertebrate host (E. antarcticus or P. murrayi or Tardigrada), microbiome type (external or internal), mat type (black or orange), stream (Canada or Bowles Creek or Delta or Von Guerard), and their interactions. The abbreviation “n.a.” is used to depict when a term was not included in the model.

A. External and Internal Microbiomes B. External Microbiomes C. Internal Microbiomes
P R2 P R2 P R2
Host <0.00 0.05 <0.00 0.09 <0.00 0.09
Microbiome <0.00 0.02 n.a. n.a. n.a. n.a.
Mat 0.53 <0.00 0.04 0.03 0.34 0.02
Stream 0.09 0.03 <0.00 0.09 0.11 0.06
Host:Microbiome <0.00 0.03 n.a. n.a. n.a. n.a.
Host:Mat 0.67 0.02 0.01 0.06 0.13 0.04
Microbiome Type:Mat 0.04 0.01 n.a. n.a. n.a. n.a.
Host:Microbiome Type:Mat <0.00 0.02 n.a. n.a. n.a. n.a.

Differences in bacterial community composition of A. all microbiomes, B. external microbiomes, and C. internal microbiomes using a PERMANOVA.

A. External and Internal Microbiomes
B. External Microbiomes
C. Internal Microbiomes
P R2 P R2 P R2
Host < 0.00 0.14 < 0.00 0.08 < 0.00 0.23
Microbiome < 0.00 0.02 n.a. n.a. n.a. n.a.
Mat < 0.00 0.01 0.02 0.03 0.23 0.01
Stream < 0.00 0.06 < 0.00 0.10 < 0.00 0.07
Host:Microbiome < 0.00 0.03 n.a. n.a. n.a. n.a.
Host:Mat 0.09 0.02 0.11 0.05 0.48 0.02
Microbiome:Mat 0.79 < 0.00 n.a. n.a. n.a. n.a.
Host:Microbiome:Mat 0.47 0.01 n.a. n.a. n.a. n.a.

Differences in relative abundance of selected taxa (Cyanobacteria, Bacteroidota, Proteobacteria, Actinobacteriota, Flavobacterium, Larkinella, Fungi, and Metazoa) using a linear mixed model. Comparisons included microinvertebrate host (E. antarcticus or P. murrayi or Tardigrada), microbiome type (external or internal), mat type (black or orange), stream (Canada or Bowles Creek or Delta or Von Guerard), and their interactions.

Cyanobacteria Bacteroidota Proteobacteria Actinobacteriota
F P F P F P F P
Host 3.85 0.03 9.06 <0.00 13.71 <0.00 5.17 0.01
Microbiome 6.84 0.01 0.76 0.39 2.38 0.13 0.36 0.55
Mat 0.02 0.88 1.13 0.30 1.21 0.28 0.62 0.44
Stream 4.92 0.01 2.74 0.07 0.28 0.84 3.24 0.05
Host: Microbiome 1.85 0.17 4.87 0.01 3.98 0.03 0.23 0.80
Host:Mat 3.09 0.06 1.88 0.17 3.26 0.05 0.15 0.86
Microbiome:Mat 0.75 0.39 0.00 0.97 <0.00 0.99 0.26 0.61
Host:Microbiome:Mat 1.93 0.16 0.32 0.73 0.31 0.73 0.10 0.90
Flavobacterium Larkinella Fungi Metazoa
F P F P F P F P
Host 6.83 <0.00 27.80 <0.00 13.10 <0.00 7.56 <0.00
Microbiome 3.44 0.07 4.73 0.03 2.23 0.14 1.15 0.28
Mat 0.16 0.69 0.00 0.95 0.24 0.63 0.15 0.70
Stream 0.40 0.76 1.59 0.23 4.01 0.02 0.69 0.57
Host: Microbiome 3.98 0.03 6.57 0.00 0.41 0.67 4.07 0.02
Host:Mat 0.94 0.40 0.17 0.84 0.27 0.77 0.30 0.74
Microbiome:Mat 0.02 0.88 0.08 0.78 0.10 0.75 1.97 0.16
Host:Microbiome:Mat 3.01 0.06 0.50 0.61 2.04 0.14 4.64 0.01

A. Means and standard error of the mean (SE) of alpha diversity metrics (Shannon’s, Simpsons, Richness, Faith’s PD) for bacterial and eukaryotic mat communities. Alpha diversity metrics were calculated with Hill Numbers. B. Differences in alpha diversity comparing mats to host microinvertebrate microbiomes using a linear mixed model to test bacterial communities and then eukaryotic communities in a second linear mixed model.

A. Mat Community Shannon’s Index Simpsons Index Richness Faith’s PD
Mean SE Mean SE Mean SE Mean SE
Bacteria 88.20 13.69 23.54 5.39 463.86 34.75 28.48 1.68
Eukaryota 8.72 1.80 4.29 0.73 73.67 8.73 13.51 0.86
B. Mat vs. Hosts Shannon’s Index Simpsons Index Richness Faith’s PD
F P F P F P F P
Bacteria 44.02 <0.01 9.16 <0.01 25.81 <0.01 56.38 <0.01
Eukaryota 2.86 0.09 8.67 <0.01 78.04 <0.01 205.88 <0.01

Differences in A. bacterial and B. eukaryotic host microbiome alpha diversity (Shannon’s, Simpsons, Richness, Faith’s PD) using a linear mixed model. Comparisons included microinvertebrate host (E. antarcticus or P. murrayi or Tardigrada), microbiome type (external or internal), mat type (black or orange), stream (Canada or Bowles Creek or Delta or Von Guerard), and their interactions. Alpha diversity metrics were based on ASVs and calculated with Hill Numbers.

A. Bacterial Diversity Shannon’s Index Simpsons Index Richness Faith’s PD
F P F P F P F P
Host 2.04 0.15 0.54 0.59 16.78 <0.00 18.01 <0.00
Microbiome 4.64 0.04 3.28 0.08 2.07 0.16 3.01 0.09
Mat 0.42 0.52 0.31 0.58 0.97 0.33 0.46 0.50
Stream 1.60 0.22 2.23 0.12 1.13 0.37 1.09 0.38
Host:Microbiome 0.34 0.71 0.2 0.82 0.45 0.64 0.28 0.75
Host:Mat 0.26 0.78 0.27 0.76 2.11 0.14 2.11 0.14
Microbiome:Mat 0.22 0.64 0.86 0.36 0.25 0.62 0.06 0.81
Host:Microbiome:Mat 1.02 0.37 0.27 0.77 0.41 0.66 0.4 0.67
B. Eukaryotic Diversity Shannon’s Index Simpsons Index Richness Faith’s PD
F P F P F P F P
Host 2.52 0.10 3.52 0.04 2.70 0.08 3.88 0.03
Microbiome 1.18 0.28 1.09 0.30 0.33 0.57 0.59 0.45
Mat 0.21 0.65 0.35 0.56 0.11 0.75 0.27 0.61
Stream 3.34 0.04 2.77 0.07 1.73 0.20 1.23 0.33
Host:Microbiome 0.26 0.77 0.27 0.77 0.44 0.64 0.85 0.44
Host:Mat 0.03 0.97 0.07 0.93 0.22 0.80 0.61 0.55
Microbiome:Mat 1.26 0.27 0.75 0.39 0.84 0.37 0.69 0.41
Host:Microbiome:Mat 0.00 1.00 0.33 0.72 0.65 0.53 0.53 0.60

Adams, B. J., Bardgett, R. D., Ayres, E., Wall, D. H., Aislabie, J., Bamforth, S., Bargagli, R., Cary, C., Cavacini, P., Connell, L., Convey, P., Fell, J. W., Frati, F., Hogg, I. D., Newsham, K. K., O’Donnell, A., Russell, N., Seppelt, R. D., and Stevens, M. I. 2006. Diversity and distribution of Victoria Land biota. Soil Biology and Biochemistry 38:3003–3018. Available at: https://doi.org/10.1016/j.soilbio.2006.04.030Adams B. J. Bardgett R. D. Ayres E. Wall D. H. Aislabie J. Bamforth S. Bargagli R. Cary C. Cavacini P. Connell L. Convey P. Fell J. W. Frati F. Hogg I. D. Newsham K. K. O’Donnell A. Russell N. Seppelt R. D. Stevens M. I. 2006 Diversity and distribution of Victoria Land biota Soil Biology and Biochemistry 383003 3018 Available at https://doi.org/10.1016/j.soilbio.2006.04.030Search in Google Scholar

Alberdi, A., and Gilbert, M. T. P. 2019. A guide to the application of Hill numbers to DNA-based diversity analyses. Molecular Ecology Resources 19:804–817. Available at: https://doi.org/10.1111/1755-0998.13014Alberdi A. Gilbert M. T. P. 2019 A guide to the application of Hill numbers to DNA-based diversity analyses Molecular Ecology Resources 19804 817 Available at https://doi.org/10.1111/1755-0998.13014Search in Google Scholar

Anderson, G. L., Caldwell, K. N., Beuchat, L. R., and Williams, P. L. 2003. Interaction of a free-living soil nematode, Caenorhabditis elegans, with surrogates of foodborne pathogenic bacteria. Journal of Food Protection 66:1543–1549. Available at: https://doi.org/10.4315/0362-028X-66.9.1543Anderson G. L. Caldwell K. N. Beuchat L. R. Williams P. L. 2003 Interaction of a free-living soil nematode, Caenorhabditis elegans, with surrogates of foodborne pathogenic bacteria Journal of Food Protection 661543 1549 Available at https://doi.org/10.4315/0362-028X-66.9.1543Search in Google Scholar

Baermann, G. 1917. A simple method for the detection of Ankylostomum (nematode) larvae in soil tests. Geneeskundig Tijdschrift voor Nederlandsch-Indie Batavia 57:131–137; (In German).Baermann G. 1917 A simple method for the detection of Ankylostomum (nematode) larvae in soil tests Geneeskundig Tijdschrift voor Nederlandsch-Indie Batavia 57131 137 In GermanSearch in Google Scholar

Blaxter, M. L., Page, A. P., Rudin, W., and Maizels, R. M. 1992. Nematode surface coats: Actively evading immunity. Parasitology Today 8(7):243–247. Available at: https://doi.org/10.1016/0169-4758(92)90126-MBlaxter M. L. Page A. P. Rudin W. Maizels R. M. 1992 Nematode surface coats: Actively evading immunity Parasitology Today 87243 247 Available at https://doi.org/10.1016/0169-4758(92)90126-MSearch in Google Scholar

Bataille, A., Lee-Cruz, L., Tripathi, B., Kim, H., and Waldman, B. 2016. Microbiome variation across amphibian skin regions: Implications for chytridiomycosis mitigation efforts. Microbial Ecology 71:221–232. Available at: https://doi.org/10.1007/s00248-015-0653-0Bataille A. Lee-Cruz L. Tripathi B. Kim H. Waldman B. 2016 Microbiome variation across amphibian skin regions: Implications for chytridiomycosis mitigation efforts Microbial Ecology 71221 232 Available at https://doi.org/10.1007/s00248-015-0653-0Search in Google Scholar

Bates, D., Mächler, M., Bolker, B., and Walker, S. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67:1–48.Bates D. Mächler M. Bolker B. Walker S. 2015 Fitting linear mixed-effects models using lme4 Journal of Statistical Software 671 48Search in Google Scholar

Bellec, L., Camon-Bonavita, M-A., Cueff-Gauchard, V., Durand, L., Gayet, L., Zeppilli, D. 2018. A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship. Frontiers in Microbiology 9:2246. Available at https://doi:10.3389/fmicb.2018.02246Bellec L. Camon-Bonavita M-A. Cueff-Gauchard V. Durand L. Gayet L. Zeppilli D. 2018 A Nematode of the Mid-Atlantic Ridge Hydrothermal Vents Harbors a Possible Symbiotic Relationship Frontiers in Microbiology 92246 Available at https://doi:10.3389/fmicb.2018.02246Search in Google Scholar

Berg, M., Stenuit, B., Ho, J., Wang, A., Parke, C., Knight, M., Alvarez-Cohen, L., and Shapira, M. 2016a. Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments. The ISME Journal 10:1998–2009. Available at: https://doi.org/10.1038/ismej.2015.253Berg M. Stenuit B. Ho J. Wang A. Parke C. Knight M. Alvarez-Cohen L. Shapira M. 2016a Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments The ISME Journal 101998 2009 Available at https://doi.org/10.1038/ismej.2015.253Search in Google Scholar

Berg, M., Zhou, X. Y., and Shapira, M. 2016b. Host-specific functional significance of Caenorhabditis Gut commensals. Frontiers in Microbiology 7:1622. Available at: https://doi.org/10.3389/fmicb.2016.01622Berg M. Zhou X. Y. Shapira M. 2016b Host-specific functional significance of Caenorhabditis Gut commensals Frontiers in Microbiology 71622 Available at https://doi.org/10.3389/fmicb.2016.01622Search in Google Scholar

Bird, A., and Bird, J. 1991. The structure of nematodes, 2nd ed. San Diego: Academic Press.Bird A. Bird J. 1991 The structure of nematodes, 2nd ed San Diego Academic PressSearch in Google Scholar

Bird, A. K., Prado-Irwin, S. R., Vredenburg, V. T., and Zink, A. G. 2018. Skin microbiomes of California terrestrial salamanders are influenced by habitat more than host phylogeny. Frontiers in Microbiology 9:442. Available at: https://doi.org/10.3389/fmicb.2018.00442Bird A. K. Prado-Irwin S. R. Vredenburg V. T. Zink A. G. 2018 Skin microbiomes of California terrestrial salamanders are influenced by habitat more than host phylogeny Frontiers in Microbiology 9442 Available at https://doi.org/10.3389/fmicb.2018.00442Search in Google Scholar

Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., Cope, E. K., Da Silva, R., Diener, C., Dorrestein, P. C., Douglas, G. M., Durall, D. M., Duvallet, C., Edwardson, C. F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J. M., Gibbons, S. M., Gibson, D. L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G. A., Janssen, S., Jarmusch, A. K., Jiang, L., Kaehler, B. D., Kang, K. B., Keefe, C. R., Keim, P., Kelley, S. T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M. G. I., Lee, J., Ley, R., Liu, Y. -X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B. D., McDonald, D., McIver, L. J., Melnik, A. V., Metcalf, J. L., Morgan, S. C., Morton, J. T., Naimey, A. T., Navas-Molina, J. A., Nothias, L. F., Orchanian, S. B., Pearson, T., Peoples, S. L., Petras, D., Preuss, M. L., Pruesse, E., Rasmussen, L. B., Rivers, A., Robeson, M. S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S. J., Spear, J. R., Swafford, A. D., Thompson, L. R., Torres, P. J., Trinh, P., Tripathi, A., Turnbaugh, P. J., Ul-Hasan, S., van der Hooft, J. J. J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K. C., Williamson, C. H. D., Willis, A. D., Xu, Z. Z., Zaneveld, J. R., Zhang, Y., Zhu, Q., Knight, R., and Caporaso, J. G. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37:852–857. Available at: https://doi.org/10.1038/s41587-019-0209-9Bolyen E. Rideout J. R. Dillon M. R. Bokulich N. A. Abnet C. C. Al-Ghalith G. A. Alexander H. Alm E. J. Arumugam M. Asnicar F. Bai Y. Bisanz J. E. Bittinger K. Brejnrod A. Brislawn C. J. Brown C. T. Callahan B. J. Caraballo-Rodríguez A. M. Chase J. Cope E. K. Da Silva R. Diener C. Dorrestein P. C. Douglas G. M. Durall D. M. Duvallet C. Edwardson C. F. Ernst M. Estaki M. Fouquier J. Gauglitz J. M. Gibbons S. M. Gibson D. L. Gonzalez A. Gorlick K. Guo J. Hillmann B. Holmes S. Holste H. Huttenhower C. Huttley G. A. Janssen S. Jarmusch A. K. Jiang L. Kaehler B. D. Kang K. B. Keefe C. R. Keim P. Kelley S. T. Knights D. Koester I. Kosciolek T. Kreps J. Langille M. G. I. Lee J. Ley R. Liu Y. -X. Loftfield E. Lozupone C. Maher M. Marotz C. Martin B. D. McDonald D. McIver L. J. Melnik A. V. Metcalf J. L. Morgan S. C. Morton J. T. Naimey A. T. Navas-Molina J. A. Nothias L. F. Orchanian S. B. Pearson T. Peoples S. L. Petras D. Preuss M. L. Pruesse E. Rasmussen L. B. Rivers A. Robeson M. S. Rosenthal P. Segata N. Shaffer M. Shiffer A. Sinha R. Song S. J. Spear J. R. Swafford A. D. Thompson L. R. Torres P. J. Trinh P. Tripathi A. Turnbaugh P. J. Ul-Hasan S. van der Hooft J. J. J. Vargas F. Vázquez-Baeza Y. Vogtmann E. von Hippel M. Walters W. Wan Y. Wang M. Warren J. Weber K. C. Williamson C. H. D. Willis A. D. Xu Z. Z. Zaneveld J. R. Zhang Y. Zhu Q. Knight R. Caporaso J. G. 2019 Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 Nature Biotechnology 37852 857 Available at https://doi.org/10.1038/s41587-019-0209-9Search in Google Scholar

Boscaro, V., Holt, C. C., Van Steenkiste, N. W. L., Herranz, M., Irwin, N. A. T., Àlvarez-Campos, P., Grzelak, K., Holovachov, O., Kerbl, A., Mathur, V., Okamoto, N., Piercey, R. S., Worsaae, K., Leander, B. S., and Keeling, P. J. 2022. Microbiomes of microscopic marine invertebrates do not reveal signatures of phylosymbiosis. Nature Microbiology 7:810–819. Available at: https://doi.org/10.1038/s41564-022-01125-9Boscaro V. Holt C. C. Van Steenkiste N. W. L. Herranz M. Irwin N. A. T. Àlvarez-CamposP. Grzelak K. Holovachov O. Kerbl A. Mathur V. Okamoto N. Piercey R. S. Worsaae K. Leander B. S. Keeling P. J. 2022 Microbiomes of microscopic marine invertebrates do not reveal signatures of phylosymbiosis Nature Microbiology 7810 819 Available at https://doi.org/10.1038/s41564-022-01125-9Search in Google Scholar

Bosch, T. C. G., and McFall-Ngai, M. J. 2011. Metaorganisms as the new frontier. Zoology 114:185–190. Available at: https://doi.org/10.1016/j.zool.2011.04.001Bosch T. C. G. McFall-Ngai M. J. 2011 Metaorganisms as the new frontier Zoology 114185 190 Available at https://doi.org/10.1016/j.zool.2011.04.001Search in Google Scholar

Brady, N. C., and Weil, R. 2014. Elements of the nature and properties of soils: Pearson new international edition, 3rd ed. Harlow: Pearson.Brady N. C. Weil R. 2014 Elements of the nature and properties of soils: Pearson new international edition, 3rd ed Harlow PearsonSearch in Google Scholar

Byrd, A. L., Belkaid, Y., and Segre, J. A. 2018. The human skin microbiome. Nature Reviews Microbiology 16:143–155. Available at: https://doi.org/10.1038/nrmicro.2017.157Byrd A. L. Belkaid Y. Segre J. A. 2018 The human skin microbiome Nature Reviews Microbiology 16143 155 Available at https://doi.org/10.1038/nrmicro.2017.157Search in Google Scholar

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., and Holmes, S. P. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13:581–583. Available at: https://doi.org/10.1038/nmeth.3869Callahan B. J. McMurdie P. J. Rosen M. J. Han A. W. Johnson A. J. A. Holmes S. P. 2016 DADA2: High-resolution sample inference from Illumina amplicon data Nature Methods 13581 583 Available at https://doi.org/10.1038/nmeth.3869Search in Google Scholar

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J., and Knight, R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7: 335–336. Available at: https://doi.org/10.1038/nmeth.f.303Caporaso J. G. Kuczynski J. Stombaugh J. Bittinger K. Bushman F. D. Costello E. K. Fierer N. Peña A. G. Goodrich J. K. Gordon J. I. Huttley G. A. Kelley S. T. Knights D. Koenig J. E. Ley R. E. Lozupone C. A. McDonald D. Muegge B. D. Pirrung M. Reeder J. Sevinsky J. R. Turnbaugh P. J. Walters W. A. Widmann J. Yatsunenko T. Zaneveld J. Knight R. 2010 QIIME allows analysis of high-throughput community sequencing data Nature Methods 7 335 336 Available at https://doi.org/10.1038/nmeth.f.303Search in Google Scholar

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S. M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J. A., Smith, G., and Knight, R. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal 6:1621–1624. Available at: https://doi.org/10.1038/ismej.2012.8Caporaso J. G. Lauber C. L. Walters W. A. Berg-Lyons D. Huntley J. Fierer N. Owens S. M. Betley J. Fraser L. Bauer M. Gormley N. Gilbert J. A. Smith G. Knight R. 2012 Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms The ISME Journal 61621 1624 Available at https://doi.org/10.1038/ismej.2012.8Search in Google Scholar

Chave, J. 2004. Neutral theory and community ecology. Ecology Letters 7:241–253. Available at: https://doi.org/10.1111/j.1461-0248.2003.00566.xChave J. 2004 Neutral theory and community ecology Ecology Letters 7241 253 Available at https://doi.org/10.1111/j.1461-0248.2003.00566.xSearch in Google Scholar

Chemnitz, C., and Weigelt, J. 2015. Soil atlas facts and figures about earth, land and fields. Potsdam: Heinrich-Böll-Stiftung.Chemnitz C. Weigelt J. 2015 Soil atlas facts and figures about earth, land and fields Potsdam Heinrich-Böll-StiftungSearch in Google Scholar

Chen, Z. X., and Dickson, D. W. 1998. Review of Pasteuria penetrans: Biology, ecology, and biological control potential. Journal of Nematology 30:313–340.Chen Z. X. Dickson D. W. 1998 Review of Pasteuria penetrans: Biology, ecology, and biological control potential Journal of Nematology 30313 340Search in Google Scholar

Chiller, K., Selkin, B. A., and Murakawa, G. J. 2001. Skin microflora and bacterial infections of the skin. Journal of Investigative Dermatology Symposium Proceedings 6:170–174. Available at: https://doi.org/10.1046/j.0022-202x.2001.00043.xChiller K. Selkin B. A. Murakawa G. J. 2001 Skin microflora and bacterial infections of the skin Journal of Investigative Dermatology Symposium Proceedings 6170 174 Available at https://doi.org/10.1046/j.0022-202x.2001.00043.xSearch in Google Scholar

Clark, L. C., and Hodgkin, J. 2014. Commensals, probiotics and pathogens in the Caenorhabditis elegans model. Cellular Microbiology 16:27–38. Available at: https://doi.org/10.1111/cmi.12234Clark L. C. Hodgkin J. 2014 Commensals, probiotics and pathogens in the Caenorhabditis elegans model Cellular Microbiology 1627 38 Available at https://doi.org/10.1111/cmi.12234Search in Google Scholar

Czerneková, M., and Vinopal, S. 2021. The tardigrade cuticle. Limnological Review 21:127–146. Available at: https://doi.org/10.2478/limre-2021-0012Czerneková M. Vinopal S. 2021 The tardigrade cuticle Limnological Review 21127 146 Available at https://doi.org/10.2478/limre-2021-0012Search in Google Scholar

Derycke, S., De Meester, N., Rigaux, A., Creer, S., Bik, H., Thomas, W. K., and Moens, T. 2016. Coexisting cryptic species of the Litoditis marina complex (Nematoda) show differential resource use and have distinct microbiomes with high intraspecific variability. Molecular Ecology 25:2093–2110. Available at: https://doi.org/10.1111/mec.13597Derycke S. De Meester N. Rigaux A. Creer S. Bik H. Thomas W. K. Moens T. 2016 Coexisting cryptic species of the Litoditis marina complex (Nematoda) show differential resource use and have distinct microbiomes with high intraspecific variability Molecular Ecology 252093 2110 Available at https://doi.org/10.1111/mec.13597Search in Google Scholar

Dinan, T. G., Stilling, R. M., Stanton, C., Cryan, J. F. 2015. Collective unconscious: How gut microbes shape human behavior. Journal of Psychiatric Research 63:1–9. Available at: https://doi.org/10.1016/j.jpsychires.2015.02.021Dinan T. G. Stilling R. M. Stanton C. Cryan J. F. 2015 Collective unconscious: How gut microbes shape human behavior Journal of Psychiatric Research 631 9 Available at https://doi.org/10.1016/j.jpsychires.2015.02.021Search in Google Scholar

Dirksen, P., Marsh, S. A., Braker, I., Heitland, N., Wagner, S., Nakad, R., Mader, S., Petersen, C., Kowallik, V., Rosenstiel, P., Félix, M. -A., and Schulenburg, H. 2016. The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model. BMCBiology 14:38. Available at: https://doi.org/10.1186/s12915-016-0258-1Dirksen P. Marsh S. A. Braker I. Heitland N. Wagner S. Nakad R. Mader S. Petersen C. Kowallik V. Rosenstiel P. Félix M. -A. Schulenburg H. 2016 The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model BMCBiology 1438 Available at https://doi.org/10.1186/s12915-016-0258-1Search in Google Scholar

Dixon, P. 2003. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science 14:927–930. Available at: https://doi.org/10.1111/j.1654-1103.2003.tb02228.xDixon P. 2003 VEGAN, a package of R functions for community ecology Journal of Vegetation Science 14927 930 Available at https://doi.org/10.1111/j.1654-1103.2003.tb02228.xSearch in Google Scholar

Doran, P. T., McKay, C. P., Clow, G. D., Dana, G. L., Fountain, A. G., Nylen, T., and Lyons, W. B. 2002. Valley floor climate observations from the McMurdo Dry Valleys, Antarctica, 1986-2000. Journal of Geophysical Research 107:4772. Available at: https://doi.org/10.1029/2001JD002045Doran P. T. McKay C. P. Clow G. D. Dana G. L. Fountain A. G. Nylen T. Lyons W. B. 2002 Valley floor climate observations from the McMurdo Dry Valleys, Antarctica, 1986-2000 Journal of Geophysical Research 1074772 Available at https://doi.org/10.1029/2001JD002045Search in Google Scholar

Douglas, A. E. 1996. Reproductive failure and the free amino acid pools in pea aphids Acyrthosiphon pisum lacking symbiotic bacteria. Journal of Insect Physiology 42:247–255.Douglas A. E. 1996 Reproductive failure and the free amino acid pools in pea aphids Acyrthosiphon pisum lacking symbiotic bacteria Journal of Insect Physiology 42247 255Search in Google Scholar

Douglas, A. E. 2014. Symbiosis as a general principle in eukaryotic evolution. Cold Spring Harbor Perspectives in Biology 6:a016113–a016113. Available at: https://doi.org/10.1101/cshperspect.a016113Douglas A. E. 2014 Symbiosis as a general principle in eukaryotic evolution Cold Spring Harbor Perspectives in Biology 6a016113 a016113 Available at https://doi.org/10.1101/cshperspect.a016113Search in Google Scholar

Eisenback, J. D. 1986. A comparison of techniques useful for preparing nematodes for scanning electron microscopy. Journal of Nematology 18:479–487.Eisenback J. D. 1986 A comparison of techniques useful for preparing nematodes for scanning electron microscopy Journal of Nematology 18479 487Search in Google Scholar

Elhady, A., Giné, A., Topalovic, O., Jacquiod, S., Sørensen, S. J., Sorribas, F. J., and Heuer, H. 2017. Microbiomes associated with infective stages of root-knot and lesion nematodes in soil. PLOS ONE 12:e0177145. Available at: https://doi.org/10.1371/journal.pone.0177145Elhady A. Giné A. Topalovic O. Jacquiod S. Sørensen S. J. Sorribas F. J. Heuer H. 2017 Microbiomes associated with infective stages of root-knot and lesion nematodes in soil PLOS ONE 12e0177145 Available at https://doi.org/10.1371/journal.pone.0177145Search in Google Scholar

Engel, K., Sauer, J., Jünemann, S., Winkler, A., Wibberg, D., Kalinowski, J., Tauch, A., and Caspers, B. A. 2018. Individual- and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing. Microbial Ecology 76:518–529. Available at: https://doi.org/10.1007/s00248-017-1130-8Engel K. Sauer J. Jünemann S. Winkler A. Wibberg D. Kalinowski J. Tauch A. Caspers B. A. 2018 Individual- and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing Microbial Ecology 76518 529 Available at https://doi.org/10.1007/s00248-017-1130-8Search in Google Scholar

Fountain, A. G., Lyons, W. B., Burkins, M. B., Dana, G. L., Doran, P. T., Lewis, K. J., McKnight, D. M., Moorhead, D. L., Parsons, A. N., Priscu, J. C., Wall, D. H., Wharton Jr., R. A., and Virginia, R. A. 1999. Physical controls on the Taylor Valley ecosystem, Antarctica. BioScience 49:961–971. Available at: https://doi.org/10.1525/bisi.1999.49.12.961Fountain A. G. Lyons W. B. Burkins M. B. Dana G. L. Doran P. T. Lewis K. J. McKnight D. M. Moorhead D. L. Parsons A. N. Priscu J. C. Wall D. H. Wharton Jr R. A. Virginia R. A. 1999 Physical controls on the Taylor Valley ecosystem, Antarctica BioScience 49961 971 Available at https://doi.org/10.1525/bisi.1999.49.12.961Search in Google Scholar

Frisch, D., Green, A. J., Figuerola, J. 2007. High dispersal capacity of a broad spectrum of aquatic invertebrates via waterbirds. Aquatic Sciences 69: 568–574. Available at: https://doi.org/10.1007/s00027-007-0915-0Frisch D. Green A. J. Figuerola J. 2007 High dispersal capacity of a broad spectrum of aquatic invertebrates via waterbirds Aquatic Sciences 69 568 574 Available at https://doi.org/10.1007/s00027-007-0915-0Search in Google Scholar

Grice, E. A., and Segre, J. A. 2011. The skin microbiome. Nature Reviews Microbiology 9:244–253. Available at: https://doi.org/10.1038/nrmicro2537Grice E. A. Segre J. A. 2011 The skin microbiome Nature Reviews Microbiology 9244 253 Available at https://doi.org/10.1038/nrmicro2537Search in Google Scholar

Guidetti, R., Vecchi, M., Ferrari, A., Newton, I. L. G., Cesari, M., and Rebecchi, L. 2019. Further insights in the Tardigrada microbiome: Phylogenetic position and prevalence of infection of four new Alphaproteobacteria putative endosymbionts. Zoological Journal of the Linnean Society 188:925–937. Available at: https://doi.org/10.1093/zoolinnean/zlz128Guidetti R. Vecchi M. Ferrari A. Newton I. L. G. Cesari M. Rebecchi L. 2019 Further insights in the Tardigrada microbiome: Phylogenetic position and prevalence of infection of four new Alphaproteobacteria putative endosymbionts Zoological Journal of the Linnean Society 188925 937 Available at https://doi.org/10.1093/zoolinnean/zlz128Search in Google Scholar

Hammer, T. J., Sanders, J. G., and Fierer, N. 2019. Not all animals need a microbiome. FEMS Microbiology Letters 366:fnz117. Available at: https://doi.org/10.1093/femsle/fnz117Hammer T. J. Sanders J. G. Fierer N. 2019 Not all animals need a microbiome FEMS Microbiology Letters 366fnz117 Available at https://doi.org/10.1093/femsle/fnz117Search in Google Scholar

Harvey, S. C. 2009. Non-dauer larval dispersal in Caenorhabditis elegans. Journal of Experimental Zoology 312B:224–230. Available at: https://doi.org/10.1002/jez.b.21287Harvey S. C. 2009 Non-dauer larval dispersal Caenorhabditis elegans. Journal of Experimental Zoology 312B224 230 Available at https://doi.org/10.1002/jez.b.21287Search in Google Scholar

Hirzel, A. H., and Lay, G. L. 2008. Habitat suitability modelling and niche theory. Journal of Applied Ecology 45:1372–1381. Available at: https://doi.org/10.1111/j.1365-2664.2008.01524.xHirzel A. H. Lay G. L. 2008 Habitat suitability modelling and niche theory Journal of Applied Ecology 451372 1381 Available at https://doi.org/10.1111/j.1365-2664.2008.01524.xSearch in Google Scholar

Hodda, M. 2022. Phylum Nematoda: feeding habits for all valid genera using a new, universal scheme encompassing the entire phylum, with descriptions of morphological characteristics of the stoma, a key, and discussion of the evidence for trophic relationships. Zootaxa 5114:318–451. Available at: https://doi.org/10.11646/zootaxa.5114.1.3Hodda M. 2022 Phylum Nematoda: feeding habits for all valid genera using a new, universal scheme encompassing the entire phylum, with descriptions of morphological characteristics of the stoma, a key, and discussion of the evidence for trophic relationships Zootaxa 5114318 451 Available at https://doi.org/10.11646/zootaxa.5114.1.3Search in Google Scholar

Jenkins, W. R. 1964. A rapid centrifugal-flotation technique for separating nematodes from soil. Plant Disease Reporter 48:692.Jenkins W. R. 1964 A rapid centrifugal-flotation technique for separating nematodes from soil Plant Disease Reporter 48692Search in Google Scholar

Kaczmarek, Ł., Roszkowska, M., Poprawa, I., Janelt, K., Kmita, H., Gawlak, M., Fiałkowska, E., and Mioduchowska, M. 2020. Integrative description of bisexual Paramacrobiotus experimentalis sp. nov. (Macrobiotidae) from republic of Madagascar (Africa) with microbiome analysis. Molecular Phylogenetics and Evolution 145:106730. Available at: https://doi.org/10.1016/j.ympev.2019.106730Kaczmarek Ł. Roszkowska M. Poprawa I. Janelt K. Kmita H. Gawlak M. Fiałkowska E. Mioduchowska M. 2020 Integrative description of bisexual Paramacrobiotus experimentalis sp. nov. (Macrobiotidae) from republic of Madagascar (Africa) with microbiome analysis Molecular Phylogenetics and Evolution 145106730 Available at https://doi.org/10.1016/j.ympev.2019.106730Search in Google Scholar

Kaplan, F., Alborn, H. T., von Reuss, S. H., Ajredini, R., Ali, J. G., Akyazi, F., Stelinski, L. L., Edison, A. S., Schroeder, F. C., and Teal, P. E. 2012. Interspecific nematode signals regulate dispersal behavior. PLoS ONE 7:e38735. Available at: https://doi.org/10.1371/journal.pone.0038735Kaplan F. Alborn H. T. von Reuss S. H. Ajredini R. Ali J. G. Akyazi F. Stelinski L. L. Edison A. S. Schroeder F. C. Teal P. E. 2012 Interspecific nematode signals regulate dispersal behavior PLoS ONE 7e38735 Available at https://doi.org/10.1371/journal.pone.0038735Search in Google Scholar

Komárek, J., Katovský, J., Ventura, S., Turicchia, S. A., and Šmarda, S. 2009. The cyanobacterial genus Phormidesmis. Algological Studies 129:41–59. Available at: https://doi.org/10.1127/1864-1318/2009/0129-0041Komárek J. Katovský J. Ventura S. Turicchia S. A. Šmarda S. 2009 The cyanobacterial genus Phormidesmis Algological Studies 12941 59 Available at https://doi.org/10.1127/1864-1318/2009/0129-0041Search in Google Scholar

Laforest-Lapointe, I., and Arrieta, M. -C. 2018. Microbial eukaryotes: A missing link in gut microbiome studies. MSystems 3:e00201–17. Available at: https://doi.org/10.1128/mSystems.00201-17Laforest-Lapointe I. Arrieta M. -C. 2018 Microbial eukaryotes: A missing link in gut microbiome studies MSystems 3e00201 17 Available at https://doi.org/10.1128/mSystems.00201-17Search in Google Scholar

Lemieux-Labonté, V., Tromas, N., Shapiro, B. J., and Lapointe, F. -J. 2016. Environment and host species shape the skin microbiome of captive neotropical bats. PeerJ 4:e2430. Available at: https://doi.org/10.7717/peerj.2430Lemieux-Labonté V. Tromas N. Shapiro B. J. Lapointe F. -J. 2016 Environment and host species shape the skin microbiome of captive neotropical bats PeerJ 4e2430 Available at https://doi.org/10.7717/peerj.2430Search in Google Scholar

Marchesi, J. R. 2010. Chapter 2 - prokaryotic and eukaryotic diversity of the human gut. Advances in Applied Microbiology 72:43–62. Available at: https://doi.org/10.1016/S0065-2164(10)72002-5Marchesi J. R. 2010 Chapter 2 - prokaryotic and eukaryotic diversity of the human gut Advances in Applied Microbiology 7243 62 Available at https://doi.org/10.1016/S0065-2164(10)72002-5Search in Google Scholar

Martin, M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal 17:10–12. Available at: https://doi.org/10.14806/ej.17.1.200Martin M. 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal 1710 12 Available at https://doi.org/10.14806/ej.17.1.200Search in Google Scholar

Martinez Arbizu, P. 2022. PairwiseAdonis: Pairwise multilevel comparison using Adonis. R Package Version 0.4. Available at: https://github.com/pmartinezarbizu/pairwiseAdonisMartinez Arbizu P. 2022 PairwiseAdonis: Pairwise multilevel comparison using Adonis R Package Version 0.4 Available at https://github.com/pmartinezarbizu/pairwiseAdonisSearch in Google Scholar

McKnight, D., Niyogia, E., Algera, L., Conovitz, E., and Tate, A. 1999. Dry Valley streams in Antarctica: Ecosystems waiting for water. BioScience 49:985–995. Available at: https://doi.org/10.1525/bisi.1999.49.12.985McKnight D. Niyogia E. Algera L. Conovitz E. Tate A. 1999 Dry Valley streams in Antarctica: Ecosystems waiting for water BioScience 49985 995 Available at https://doi.org/10.1525/bisi.1999.49.12.985Search in Google Scholar

McQueen, J. P., Gattoni, K., Gendron, E., Schmidt, S. K., Sommers, P., and Porazinska, D. L. 2022. Host identity is the dominant factor in the assembly of nematode and tardigrade gut microbiomes in Antarctic Dry Valley streams. Scientific Reports. 12:20118. Available at https://doi.org/10.1038/s41598-022-24206-5McQueen J. P. Gattoni K. Gendron E. Schmidt S. K. Sommers P. Porazinska D. L. 2022 Host identity is the dominant factor in the assembly of nematode and tardigrade gut microbiomes in Antarctic Dry Valley streams Scientific Reports 1220118 Available at https://doi.org/10.1038/s41598-022-24206-5Search in Google Scholar

Mioduchowska, M., Nitkiewicz, B., Roszkowska, M., Kačarević, U., Madanecki, P., Pinceel, T., Namiotko, T., Gołdyn, B., and Kaczmarek, Ł. 2021. Taxonomic classification of the bacterial endosymbiont Wolbachia based on next-generation sequencing: Is there molecular evidence for its presence in tardigrades? Genome 64:951–958. Available at: https://doi.org/10.1139/gen-2020-0036Mioduchowska M. Nitkiewicz B. Roszkowska M. Kačarević U. Madanecki P. Pinceel T. Namiotko T. Gołdyn B. Kaczmarek Ł. 2021 Taxonomic classification of the bacterial endosymbiont Wolbachia based on next-generation sequencing: Is there molecular evidence for its presence in tardigrades? Genome 64951 958 Available at https://doi.org/10.1139/gen-2020-0036Search in Google Scholar

Muletz Wolz, C. R., Yarwood, S. A., Grant, E. H. C., Fleischer, R. C., and Lips, K. R. 2018. Effects of host species and environment on the skin microbiome of Plethodontid salamanders. Journal of Animal Ecology 87:341–353. Available at: https://doi.org/10.1111/1365-2656.12726Muletz Wolz C. R. YarwoodS. A. GrantE. H. C. FleischerR. C. LipsK. R. 2018 Effects of host species and environment on the skin microbiome of Plethodontid salamanders Journal of Animal Ecology 87341 353 Available at https://doi.org/10.1111/1365-2656.12726Search in Google Scholar

Nemergut, D. R., Schmidt, S. K., Fukami, T., O’Neill, S. P., Bilinski, T. M., Stanish, L. F., Knelman, J. E., Darcy, J. L., Lynch, R. C., Wickey, P., and Ferrenberg, S. 2013. Patterns and processes of microbial community assembly. Microbiology and Molecular Biology Reviews 77:342–356. Available at: https://doi.org/10.1128/MMBR.00051-12Nemergut D. R. Schmidt S. K. Fukami T. O’Neill S. P. Bilinski T. M. Stanish L. F. Knelman J. E. Darcy J. L. Lynch R. C. Wickey P. Ferrenberg S. 2013 Patterns and processes of microbial community assembly Microbiology and Molecular Biology Reviews 77342 356 Available at https://doi.org/10.1128/MMBR.00051-12Search in Google Scholar

Nkem, J. N., Wall, D. H., Virginia, R. A., Barrett, J. E., Broos, E. M., Porazinska, D. L., and Adams, B. J. 2006. Wind dispersal of soil invertebrates in the McMurdo Dry Valleys, Antarctica. Polar Biology 29:346–352. Available at: https://doi.org/10.1007/s00300-005-0061-xNkem J. N. Wall D. H. Virginia R. A. Barrett J. E. Broos E. M. Porazinska D. L. Adams B. J. 2006 Wind dispersal of soil invertebrates in the McMurdo Dry Valleys, Antarctica Polar Biology 29346 352 Available at https://doi.org/10.1007/s00300-005-0061-xSearch in Google Scholar

Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Cáceres, M., Durand, S., Evangelista, H. B. A., FitzJohn, R., Friendly, M., Furneaux, B., Hannigan, G., Hill, M. O., Lahti, L., McGlinn, D., Ouellette, M. -H., Cunha, E. R., Smith, T., Stier, A., ter Braak, C. J. F., and Weedon, J. 2019. Vegan: Community ecology package. Available at: https://CRAN.R-project.org/package=veganOksanen J. Simpson G. L. Blanchet F. G. Kindt R. Legendre P. Minchin P. R. O’Hara R. B. Solymos P. Stevens M. H. H. Szoecs E. Wagner H. Barbour M. Bedward M. Bolker B. Borcard D. Carvalho G. Chirico M. De Cáceres M. Durand S. Evangelista H. B. A. FitzJohn R. Friendly M. Furneaux B. Hannigan G. Hill M. O. Lahti L. McGlinn D. Ouellette M. -H. Cunha E. R. Smith T. Stier A. ter Braak C. J. F. Weedon J. 2019 Vegan: Community ecology package Available at https://CRAN.R-project.org/package=veganSearch in Google Scholar

O’Neill, C. A., Monteleone, G., McLaughlin, J. T., and Paus, R. 2016. The gut-skin axis in health and disease: A paradigm with therapeutic implications. BioEssays 38:1167–1176. Available at: https://doi.org/10.1002/bies.201600008O’Neill C. A. Monteleone G. McLaughlin J. T. Paus R. 2016 The gut-skin axis in health and disease: A paradigm with therapeutic implications BioEssays 381167 1176 Available at https://doi.org/10.1002/bies.201600008Search in Google Scholar

Peixoto, R. S., Harkins, D. M., and Nelson, K. E. 2021. Advances in microbiome research for animal health. Annual Review of Animal Biosciences 9: 289–311. Available at: https://doi.org/10.1146/annurev-animal-091020-075907Peixoto R. S. Harkins D. M. Nelson K. E. 2021 Advances in microbiome research for animal health Annual Review of Animal Biosciences 9 289 311 Available at https://doi.org/10.1146/annurev-animal-091020-075907Search in Google Scholar

Porazinska, D. L., Wall, D. H., and Virginia, R. A. 2002. Population age structure of nematodes in the Antarctic Dry Valleys: Perspectives on time, space, and habitat suitability. Arctic, Antarctic, and Alpine Research 34:159–168. Available at: https://doi.org/10.1080/15230430.2002.12003480Porazinska D. L. Wall D. H. Virginia R. A. 2002 Population age structure of nematodes in the Antarctic Dry Valleys: Perspectives on time, space, and habitat suitability Arctic, Antarctic, and Alpine Research 34159 168 Available at https://doi.org/10.1080/15230430.2002.12003480Search in Google Scholar

Prado-Irwin, S. R., Bird, A. K., Zink, A. G., and Vredenburg, V. T. 2017. Intraspecific variation in the skin-associated microbiome of a terrestrial salamander. Microbial Ecology 74:745–756. Available at: https://doi.org/10.1007/s00248-017-0986-yPrado-Irwin S. R. Bird A. K. Zink A. G. Vredenburg V. T. 2017 Intraspecific variation in the skin-associated microbiome of a terrestrial salamander Microbial Ecology 74745 756 Available at https://doi.org/10.1007/s00248-017-0986-ySearch in Google Scholar

Ptatscheck, C., Gansfort, B., and Traunspurger, W. 2018. The extent of wind-mediated dispersal of small metazoans, focusing nematodes. Scientific Reports 8:6814. Available at: https://doi.org/10.1038/s41598-018-24747-8Ptatscheck C. Gansfort B. Traunspurger W. 2018 The extent of wind-mediated dispersal of small metazoans, focusing nematodes Scientific Reports 86814 Available at https://doi.org/10.1038/s41598-018-24747-8Search in Google Scholar

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., and Glöckner, F. O. 2013. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research 41 :D590–D596. Available at: https://doi.org/10.1093/nar/gks1219Quast C. Pruesse E. Yilmaz P. Gerken J. Schweer T. Yarza P. Peplies J. Glöckner F. O. 2013 The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools Nucleic Acids Research 41 D590 D596 Available at https://doi.org/10.1093/nar/gks1219Search in Google Scholar

R Core Team. 2020. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available at: https://www.R-project.org/R Core Team 2020 R: A language and environment for statistical computing Vienna R Foundation for Statistical Computing Available at https://www.R-project.org/Search in Google Scholar

Raabová, L., Kovacik, L., Elster, J., and Strunecký, O. 2019. Review of the genus Phormidesmis (Cyanobacteria) based on environmental, morphological, and molecular data with description of a new genus Leptodesmis. Phytotaxa 395:1–16. Available at: https://doi.org/10.11646/phytotaxa.395.1.1Raabová L. Kovacik L. Elster J. Strunecký O. 2019 Review of the genus Phormidesmis (Cyanobacteria) based on environmental, morphological, and molecular data with description of a new genus Leptodesmis Phytotaxa 3951 16 Available at https://doi.org/10.11646/phytotaxa.395.1.1Search in Google Scholar

Rae, R., Riebesell, M., Dinkelacker, I., Wang, Q., Herrmann, M., Weller, A. M., Dieterich, C., and Sommer, R. J. 2008. Isolation of naturally associated bacteria of necromenic Pristionchus nematodes and fitness consequences. Journal of Experimental Biology 211:1927– 1936. Available at: https://doi.org/10.1242/jeb.014944Rae R. Riebesell M. Dinkelacker I. Wang Q. Herrmann M. Weller A. M. Dieterich C. Sommer R. J. 2008 Isolation of naturally associated bacteria of necromenic Pristionchus nematodes and fitness consequences Journal of Experimental Biology 2111927 1936 Available at https://doi.org/10.1242/jeb.014944Search in Google Scholar

Raymann, K., and Moran, N. A. 2018. The role of the gut microbiome in health and disease of adult honey bee workers. Current Opinion in Insect Science 26:97–104. Available at: https://doi.org/10.1016/j.cois.2018.02.012Raymann K. Moran N. A. 2018 The role of the gut microbiome in health and disease of adult honey bee workers Current Opinion in Insect Science 2697 104 Available at https://doi.org/10.1016/j.cois.2018.02.012Search in Google Scholar

Rivera, D. E., Lažetić, V., Troemel, E. R., and Luallen, R. J. 2022. RNA fluorescence in situ hybridization (FISH) to visualize microbial colonization and infection in the Caenorhabditis elegans intestines. Microbiology. Available at: https://doi.org/10.1101/2022.02.26.482129Rivera D. E. Lažetić V. Troemel E. R. Luallen R. J. 2022 RNA fluorescence in situ hybridization (FISH) to visualize microbial colonization and infection in the Caenorhabditis elegans intestines Microbiology Available at https://doi.org/10.1101/2022.02.26.482129Search in Google Scholar

Ross, A. A., Müller, K. M., Weese, J. S., and Neufeld, J. D. 2018. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proceedings of the National Academy of Sciences 115:E5786–E5795. Available at: https://doi.org/10.1073/pnas.1801302115Ross A. A. Müller K. M. Weese J. S. Neufeld J. D. 2018 Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia Proceedings of the National Academy of Sciences 115E5786 E5795 Available at https://doi.org/10.1073/pnas.1801302115Search in Google Scholar

Ross, A. A., Hoffmann, A. R., and Neufeld, J. D. 2019. The skin microbiome of vertebrates. Microbiome 7:79. Available at: https://doi.org/10.1186/s40168-019-0694-6Ross A. A. Hoffmann A. R. Neufeld J. D. 2019 The skin microbiome of vertebrates Microbiome 779 Available at https://doi.org/10.1186/s40168-019-0694-6Search in Google Scholar

Salem, I., Ramser, A., Isham, N., and Ghannoum, M. A. 2018. The gut microbiome as a major regulator of the gut-skin axis. Frontiers in Microbiology 9:1459. Available at: https://doi.org/10.3389/fmicb.2018.01459Salem I. Ramser A. Isham N. Ghannoum M. A. 2018 The gut microbiome as a major regulator of the gut-skin axis Frontiers in Microbiology 91459 Available at https://doi.org/10.3389/fmicb.2018.01459Search in Google Scholar

Schill, R. O. 2018. Water bears: The biology of tardigrades, vol. 2. Cham: Springer International Publishing.Schill R. O. 2018 Water bears: The biology of tardigrades, vol. 2 Cham Springer International PublishingSearch in Google Scholar

Schuelke, T., Pereira, T. J., Hardy, S. M., and Bik, H. M. 2018. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Molecular Ecology 27:1930–1951. Available at: https://doi.org/10.1111/mec.14539Schuelke T. Pereira T. J. Hardy S. M. Bik H. M. 2018 Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats Molecular Ecology 271930 1951 Available at https://doi.org/10.1111/mec.14539Search in Google Scholar

Searle, S. R., Speed, F. M., and Milliken, G. A. 1980. Population marginal means in the linear model: An alternative to least squares means. The American Statistician 34:216–221.Searle S. R. Speed F. M. Milliken G. A. 1980 Population marginal means in the linear model: An alternative to least squares means The American Statistician 34216 221Search in Google Scholar

Seinhorst, J. W. 1962. Modifications of the elutriation method for extracting nematodes from soil. Nematologica 8:117–128. Available at: https://doi.org/10.1163/187529262X00332Seinhorst J. W. 1962 Modifications of the elutriation method for extracting nematodes from soil Nematologica 8117 128 Available at https://doi.org/10.1163/187529262X00332Search in Google Scholar

Sender, R., Fuchs, S., and Milo, R. 2016. Revised estimates for the number of human and bacteria cells in the body. PLOS Biology 14:e1002533. Available at: https://doi.org/10.1371/journal.pbio.1002533Sender R. Fuchs S. Milo R. 2016 Revised estimates for the number of human and bacteria cells in the body PLOS Biology 14e1002533 Available at https://doi.org/10.1371/journal.pbio.1002533Search in Google Scholar

Shapiro, D. I., Berry, E. C., and Lewis, L. C. 1993. Interactions between nematodes and earthworms: Enhanced dispersal of Steinernema carpocapsae. Journal of Nematology 25:189–192.Shapiro D. I. Berry E. C. Lewis L. C. 1993 Interactions between nematodes and earthworms: Enhanced dispersal of Steinernema carpocapsae Journal of Nematology 25189 192Search in Google Scholar

Shaw, E. A., Adams, B. J., Barrett, J. E., Lyons, W. B., Virginia, R. A., and Wall, D. H. 2018. Stable C and N isotope ratios reveal soil food web structure and identify the nematode Eudorylaimus antarcticus as an omnivore-predator in Taylor Valley, Antarctica. Polar Biology 41:1013–1018. Available at: https://doi.org/10.1007/s00300-017-2243-8Shaw E. A. Adams B. J. Barrett J. E. Lyons W. B. Virginia R. A. Wall D. H. 2018 Stable C and N isotope ratios reveal soil food web structure and identify the nematode Eudorylaimus antarcticus as an omnivore-predator in Taylor Valley, Antarctica Polar Biology 411013 1018 Available at https://doi.org/10.1007/s00300-017-2243-8Search in Google Scholar

Tan, L., and Darby, C. 2004. A movable surface: Formation of Yersinia sp. biofilms on motile Caenorhabditis elegans. Journal of Bacteriology 186:5087–5092. Available at: https://doi.org/10.1128/JB.186.15.5087-5092.2004Tan L. Darby C. 2004 A movable surface: Formation of Yersinia sp. biofilms on motile Caenorhabditis elegans Journal of Bacteriology 1865087 5092 Available at https://doi.org/10.1128/JB.186.15.5087-5092.2004Search in Google Scholar

Thomazeau, S., Houdan-Fourmont, A., Couté, A., Duval, C., Couloux, A., Rousseau, F., and Bernard, C. 2010. The contribution of Sub-Saharan African strains to the phylogeny of cyanobacteria: Focusing on the Nostocaceae (Nostocales, Cyanobacteria). Journal of Phycology 46:564–579. Available at: https://doi.org/10.1111/j.1529-8817.2010.00836.xThomazeau S. Houdan-Fourmont A. Couté A. Duval C. Couloux A. Rousseau F. Bernard C. 2010 The contribution of Sub-Saharan African strains to the phylogeny of cyanobacteria: Focusing on the Nostocaceae (Nostocales, Cyanobacteria) Journal of Phycology 46564 579 Available at https://doi.org/10.1111/j.1529-8817.2010.00836.xSearch in Google Scholar

Thursby, E., Juge N. 2017. Introduction to the human gut microbiota. Biochemical Journal 474:1823–1836. Available at: https://doi.org/10.1042/BCJ20160510Thursby E. Juge N. 2017 Introduction to the human gut microbiota Biochemical Journal 4741823 1836 Available at https://doi.org/10.1042/BCJ20160510Search in Google Scholar

Treonis, A. M., Wall, D. H., and Virginia, R. A. 1999. Invertebrate biodiversity in Antarctic Dry Valley soils and sediments. Ecosystems 2:482–492. Available at: https://doi.org/10.1007/s100219900096Treonis A. M. Wall D. H. Virginia R. A. 1999 Invertebrate biodiversity in Antarctic Dry Valley soils and sediments Ecosystems 2482 492 Available at https://doi.org/10.1007/s100219900096Search in Google Scholar

Vafeiadou, A-M., Derycke, S., Rigaux, A., De Meester, N., Guden, R. M., Moens, T. 2022. Microbiome Differentiation Among Coexisting Nematode Species in Estuarine Microhabitats: A Metagenetic Analysis. Frontiers in Marine Ecosystem Ecology 9:881566. Available at: https://doi.org/10.3389/fmars.2022.881566Vafeiadou A-M. Derycke S. Rigaux A. De Meester N. Guden R. M. Moens T. 2022 Microbiome Differentiation Among Coexisting Nematode Species in Estuarine Microhabitats: A Metagenetic Analysis Frontiers in Marine Ecosystem Ecology 9881566 Available at https://doi.org/10.3389/fmars.2022.881566Search in Google Scholar

Vanschoenwinkel, B., Waterkeyn, A., Vandecaetsbeek, T., Pineau, O., Grillas, P., and Brendonck, L. 2008. Dispersal of freshwater invertebrates by large terrestrial mammals: A case study with wild boar Sus scrofa in Mediterranean wetlands. Freshwater Biology 53(11):2264–2273. Available at: https://doi.org/10.1111/j.1365-2427.2008.02071.xVanschoenwinkel B. Waterkeyn A. Vandecaetsbeek T. Pineau O. Grillas P. Brendonck L. 2008 Dispersal of freshwater invertebrates by large terrestrial mammals: A case study with wild boar Sus scrofa in Mediterranean wetlands Freshwater Biology 53112264 2273 Available at https://doi.org/10.1111/j.1365-2427.2008.02071.xSearch in Google Scholar

Vecchi, M., Newton, I. L. G., Cesari, M., Rebecchi, L., and Guidetti, R. 2018. The microbial community of tardigrades: Environmental influence and species specificity of microbiome structure and composition. Microbial Ecology 76:467–481. Available at: https://doi.org/10.1007/s00248-017-1134-4Vecchi M. Newton I. L. G. Cesari M. Rebecchi L. Guidetti R. 2018 The microbial community of tardigrades: Environmental influence and species specificity of microbiome structure and composition Microbial Ecology 76467 481 Available at https://doi.org/10.1007/s00248-017-1134-4Search in Google Scholar

Wall, D. H. 2007. Global change tipping points: above- and below-ground biotic interactions in a low diversity ecosystem. Philosophical Transactions of the Royal Society B 362:2291–2306. Available at: https://doi.org/10.1098/rstb.2006.1950Wall D. H. 2007 Global change tipping points: above- and below-ground biotic interactions in a low diversity ecosystem Philosophical Transactions of the Royal Society B 3622291 2306 Available at https://doi.org/10.1098/rstb.2006.1950Search in Google Scholar

Whitehead, A. G., and J. R. Hemming. 1965. A comparison of some quantitative methods of extracting small vermiform nematodes from soil. Annals of Applied Biology 55:25–38. Available at: https://doi.org/10.1111/j.1744-7348.1965.tb07864.xWhitehead A. G. Hemming J. R. 1965 A comparison of some quantitative methods of extracting small vermiform nematodes from soil Annals of Applied Biology 5525 38 Available at https://doi.org/10.1111/j.1744-7348.1965.tb07864.xSearch in Google Scholar

Wickham, H. 2016. Ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag. Available at: https://ggplot2.tidyverse.org/Wickham H. 2016 Ggplot2: Elegant graphics for data analysis New York Springer-Verlag Available at https://ggplot2.tidyverse.org/Search in Google Scholar

Yang, P., and van Elsas, J. D. 2018. Mechanisms and ecological implications of the movement of bacteria in soil. Applied Soil Ecology 129:112–120. Available at: https://doi.org/10.1016/j.apsoil.2018.04.014Yang P. van Elsas J. D. 2018 Mechanisms and ecological implications of the movement of bacteria in soil Applied Soil Ecology 129112 120 Available at https://doi.org/10.1016/j.apsoil.2018.04.014Search in Google Scholar

Yeates, G. W. 1970. Two terrestrial nematodes from the McMurdo Sound Region, Antarctica, with a note on Anaplectus arenicola killick, 1964. Journal of Helminthology 44:27–34. Available at: https://doi.org/10.1017/S0022149X00021416Yeates G. W. 1970 Two terrestrial nematodes from the McMurdo Sound Region, Antarctica, with a note on Anaplectus arenicola killick, 1964 Journal of Helminthology 4427 34 Available at https://doi.org/10.1017/S0022149X00021416Search in Google Scholar

Yeates, G. W., Bongers, T., Goede, R., Georgieva. S. 1993. Feeding Habits in Soil Nematode Families and Genera-An Outline for Soil Ecologists. Journal of Nematology 25:315-331 17Yeates G. W. Bongers T. Goede R. Georgieva S. 1993 Feeding Habits in Soil Nematode Families and Genera-An Outline for Soil Ecologists Journal of Nematology 25315 331 17Search in Google Scholar

Zawierucha, K., Trzebny, A., Buda, J., Bagshaw, E., Franzetti, A., Dabert, M., and Ambrosini, R. 2022. Trophic and symbiotic links between obligate-glacier water bears (Tardigrada) and cryoconite microorganisms. PLOS ONE 17:e0262039. Available at: https://doi.org/10.1371/journal.pone.0262039Zawierucha K. Trzebny A. Buda J. Bagshaw E. Franzetti A. Dabert M. Ambrosini R. 2022 Trophic and symbiotic links between obligate-glacier water bears (Tardigrada) and cryoconite microorganisms PLOS ONE 17e0262039 Available at https://doi.org/10.1371/journal.pone.0262039Search in Google Scholar

Zhang, F., Berg, M., Dierking, K., Félix, M.-A., Shapira, M., Samuel, B. S., and Schulenburg, H. 2017. Caenorhabditis elegans as a model for microbiome research. Frontiers in Microbiology 8:485. Available at: https://doi.org/10.3389/fmicb.2017.00485Zhang F. Berg M. Dierking K. Félix M.-A. Shapira M. Samuel B. S. Schulenburg H. 2017 Caenorhabditis elegans as a model for microbiome research Frontiers in Microbiology 8485 Available at https://doi.org/10.3389/fmicb.2017.00485Search in Google Scholar

Zhou, J., and Ning, D. 2017. Stochastic community assembly: Does it matter in microbial ecology? Microbiology and Molecular Biology Reviews 81 :e00002-17. Available at: https://doi.org/10.1128/MMBR.00002-17Zhou J. Ning D. 2017 Stochastic community assembly: Does it matter in microbial ecology? Microbiology and Molecular Biology Reviews 81 e00002 17 Available at https://doi.org/10.1128/MMBR.00002-17Search in Google Scholar

Recommended articles from Trend MD