1 Introduction

Mountain Cloud Forests (MCF) harbor an immense biodiversity and a high percentage of endemisms, despite its restricted extension (1% of the Mexican territory) and fragmented distribution (restricted to altitudes between 600 and 3,500 masl) (Rzedowski 1996; Bubb et al. 2004). This is due to characteristic environmental conditions such as constant cloud immersion, rugged relief, low luminosity, and the convergence of boreal and neotropical species (Gual-Díaz and Rendón-Correa 2014). Due to the continuous disturbance and destruction of these forests, MCF have been recognized worldwide as a priority for conservation since they play a very important role in hydrological cycles and are biodiversity hotspots (Bubb et al. 2004; FAO 2006; CONABIO 2010).

The inventory of the diversity in MCF has increased for groups of higher plants and animals (i.e. mammals and birds). However, the characterization of the belowground diversity is still in progress (Rzedowski 1996; Bubb et al. 2004; CONABIO 2010). Members of this edaphic community such as algae, protists, nematodes, and microarthropods, hereafter referred to as microeukaryotes, participate as key elements in the degradation of organic matter and nutrient cycling (Hättenschwiler et al. 2005; Jacquiod et al. 2016). For example, nematodes and protists regulate fungal and bacterial populations by preying on them. This causes changes in community composition and activity of these microorganisms, and hence affects decomposition processes, nutrient turnover, and plant-microbiota interactions (Bonkowski 2004; Thakur and Geisen 2019). In addition, algae (i.e. glaucophytes, rhodophytes, chlorophytes and streptophytes) participate in the incorporation of carbon and nitrogen in the early stages of soil formation and serve as food for other microeukaryotes (Thorn and Lynch 2007; Friedl and Rybalka 2012; Jacquiod et al. 2016).

Around 60% of the nutrients ingested by soil microeukaryotes are excreted to the soil, having a direct effect on the nutrient cycles (Bardgett and Griffiths 1997; Hättenschwiler et al. 2005; Bardgett 2005). For example, protists release through their excreta N that is immobilized by bacteria, incrementing available N in soil by 37% (Koller et al. 2013). Particularly, microarthropods (i.e. mites, springtails) consume, fragment, and process the organic matter that enters the system, facilitating its future decomposition. In addition, when moving, they not only modify the physical structure of the microhabitat, altering the microenvironmental conditions and creating niches for microorganisms, but also transport bacteria and fungi, favoring colonization in new sites (Hättenschwiler 2005).

Despite their important role in soil trophic webs, in Mexico, of the approximately 1,400 recorded species of protists, only 36 have been isolated from soils (Gallegos-Neyra et al. 2014; Mayén-Estrada et al. 2014a, b). For algae, of the more than 2,000 reported species, only 4 come from terrestrial habitats (Pedroche et al. 1993; Novelo 2012). For both groups, the records are biased towards freshwater and marine ecosystems. In addition, due to their small size and the scarcity of genetic/molecular work conducted for these groups, records of edaphic species in Mexico are rare (Gallegos-Neyra et al. 2014). Regarding the microfauna of the MCF, most of the studies rely on direct extraction of organisms and their morphological identification, highlighting the presence of Coleoptera, Diptera, Hemiptera, Hymenoptera, Orthoptera; as well as Acari and Collembola (Parada-Barrera 1987; Morón-Ríos and Huerta-Lwanga 2006; Bernal-Rojas et al. 2009; De la Rosa and Negrete-Yankelevich 2012; Beltrán-Villanueva et al. 2016).

Metabarcoding (simultaneous identification of multiple taxa from an environmental DNA sample) presents several advantages in the study of edaphic communities since it does not involve the extraction, manipulation and description of organisms; and succeeds in the detection of cryptic, rare and non-cultivable species. This approach allows a quick and precise determination of the relative abundance of organisms and diversity patterns (Hamilton et al. 2009; Drummond et al. 2015; Ruppert et al. 2019; Lin et al. 2020), increasing the knowledge generated from traditional methodologies.

In this work we implemented a metabarcoding approach of the ITS1 region of ribosomal DNA to describe the diversity and structure of soil microeukaryotic communities in relation to key environmental variables in a pristine location of MCF in Mexico. We expect that this proxy will allow the detection of a high microeukariotic diversity at the small spatial scale (within a 10 m triangular transect) in accordance with former reports acknowledging this ecosystem as diversity hotspot, and that large diversity patters will be identified in relation to key edaphic variables paving the way for further comprehensive research on specific taxa. As a result, we report 87 protist, 35 microfaunal and 16 algal ASVs. We detected differences in community structure comparing three sites influenced by different plant species: Oreomunnea mexicana, Alsophila salvinii, and a decaying trunk. These differences were explained by the particular environmental variables of each site, mainly pH, dissolved organic carbon, ammonium, and the activity of enzymes such as polyphenol oxidase and β -glucosidase. In addition to environmental variables, the differences can also be explained by possible interactions between microeukaryotes and other microorganisms such as bacteria and fungi on which they feed.

2 Materials and methods

2.1 Study site and Sampling

Sampling was carried out in the MCF located in El Relámpago Mount (17° 35′ 30.4″ N, 96° 23′ 57.1″ W), Santiago Comaltepec, Oaxaca, Mexico. This forest belongs to the largest and most continuous patch of MCF in Mexico, an area of critical priority for conservation (Toledo-Aceves et al. 2011; del Mar et al. 2015). It is characterized by humid temperate climate with temperatures ranging from 10–26 °C, annual rainfall of 800–4,000 mm (INEGI 2005). Dominant soil group is acidic humic Acrisol (Sundue 2017).

Three sampling sites were established along an equilateral 10 m-triangular transect (spatial sampling method that increases the chance of achieving a high interstrata variance; Bąk 2014) at 2,219 masl. The first vertex was located adjacent to the base of an individual of O. mexicana (Juglandaceae), the second point next to an individual of the fern A. salvinii (Cyatheaceae), and the third point under a decomposing tree trunk. This sampling design was chosen to better represent the heterogeneity of the site. In each plot a 1 m x 1 m x 1 m triangular subplot was delimited, where three soil subsamples were collected on each vertex. Soil cores were collected at a depth of 25 cm (excluding organic horizon) using a 10 cm diameter corer. Subsamples were placed in hermetically sealed plastic bags, kept in the dark at a temperature of 4 °C for analysis in the laboratory.

2.2 Environmental DNA isolation and sequencing

Direct DNA extraction of 0.25 g from each subsample was obtained using the DNeasy PowerSoil extraction kit (Qiagen) following the manufacturer's instructions. The DNA was quantified using the Qubit® 2.0 Fluorometer (Invitrogen by Life Technologies) and sequenced using the Illumina MiSeq (2 × 300) paired-end format at the Genomic Services Laboratory (LANGEBIO, Irapuato, Guanajuato, Mexico). The ITS region (rDNA) was amplified using the primer set ITS1-F and ITS2 (Gardes and Bruns 1993), with a yield of about 25,000 reads per sample.

2.3 Bioinformatics analysis

The ITS reads were processed with the dada2 v1.13.1 (Callahan et al. 2016) package of R v4.0.2 (R Core Team 2020). Firstly, the primers and adapters were eliminated and then the ASVs (amplicon sequence variant) were determined. Sequences were filtered, excluding reads with bases other than G, C, T, and A (maxN = 0) and keeping those that presented < 1 expected errors, considering their quality scores (maxEE = c (1,1), truncQ = 3). For each subsample, the ASV inference was made for the reads in both directions (5′ 3 '-3′ 5'), considering the specific error rates of each run. Subsequently, the paired sequences that presented a minimum of 50 bp of overlap were assembled and those that did not coincide completely in the overlapping region were eliminated. Finally, chimeras were eliminated with the “removeBimeraDenovo” function of dada2 and singletons (unique sequence between readings) were excluded. To filter the microeukaryotic sequences, the ITS2 Database was used via a Basic Local Alignment Search Tool (BLAST) to identify significant similarities. Sequences matching to unidentified organisms, fungal species or higher plants, were discarded from the analysis; and only those that paired with protists, algae, nematodes and microarthropods were retained.

To attain a more precise identification of the organisms (i.e. phylum or order), reference sequences from the GenBank were incorporated in character matrices and aligned using MUSCLE v3.8.31 (Edgar 2004). Phylogenetic trees were built using the principle of parsimony using the MESQUITE version 3.61 program (Madisson and Madisson 2019). We performed heuristic searches by the Subtree pruning and regrafting (SPR) algorithm. Branch support was assessed by bootstrap analysis (100 replicates). We recovered the most parsimonious tree by a majority-rule consensus.

2.4 Data analyses

Alpha diversity (α) was calculated using the Shannon-Wiener and Simpson Indexes. The beta diversity (β) was calculated by means of the Jaccard Index and the Bray-Curtis distances. Also, to explore the relationship between the abundance of ASVs and environmental variables (pH, dissolved carbon, dissolved phosphorus, ammonium, and enzymatic activity of polyphenol oxidase, β-glucosidase, and phosphomonoesterase; data synchronously obtained from the exact same sites and retrieved from Velez et al. 2021, significant variables were chosen after a redundancy analysis) a Canonical Correspondence Analysis (CCA) was performed. These analyses were run using the vegan package (Oksanen et al. 2020) in the R v4.0.2 program (R Core Team 2020). Lastly, an Olmsted-Tukey corner test (Sokal and Rohlf 1981) was performed to visualize the community structure recognizing dominant, constant, occasional and rare species (González-Acosta et al. 2005). It was calculated and plotted in Excel using the natural logarithm of the abundances and the frequencies of appearance of each ASV.

3 Results

Through Illumina MiSeq sequencing, 682,361 raw ITS1 reads were obtained. After quality filtering 178,089 reads were removed, resulting in 504,272 retained reads that were further analyzed. Subsequently, 2771 ASVs were inferred, out of which 668 ASVs paired with microeukaryotic sequences. Particularly: 103 ASVs matched with animalia, 38 ASVs with algae (plantae), 330 ASVs with protists and 197 ASVs matched with various microeukaryotic groups. The ASV delimitation was further refined using a phylogenetic approach, leading to the identification of 138 ASVs. Protists represented 63% of the total richness, followed by animals with 25% and plantae representing only 12%.

3.1 Diversity patterns

3.1.1 Animalia

The animal fraction of microeukaryotes was composed of 15 nematodes ASVs, 15 arthropods, 2 mites and 3 platyhelminthes. Samples from site 1 (O. mexicana) and site 3 (trunk) presented the dominance of the ASV Nematoda 1. On the other hand, the structure of the community in site 2 (A. salvinii) presented a greater representation of arthropods, hexapods and different nematode ASVs with relative abundances in similar proportions to each other (Fig. 1). Regarding α diversity patterns, site 1 presented communities with high Simpson dominance, especially in subsample 1. On the contrary, for subsamples 5 and 6 of site 2, Simpson dominance was low, presenting the highest diversity values in the Inverse Simpson and Shannon Index (Table 1).

Fig. 1
figure 1

Diversity of ASVs corresponding to Animalia (microfauna). The x axis shows the three sites with their respective subsamples and the y axis indicates the relative abundance of the ASVs. The red numbers above the bars indicate the total number of ASVs identified in the subsample

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Table 1 Summary of the results produced by the diversity indexes (Simpson, Simpson's Inverse and Shannon) for each subsample (site 1, 1–3; site 2, 4–6 and site 3, 7–9) for Animalia
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When analyzing β diversity, a high heterogeneity was observed on ASV composition between sites and subsamples (Fig. 2A). Nonetheless, regarding the abundances, site 1 and 3 differed from site 2 (Fig. 2B). The structure of the microfauna community was mostly represented by 23 rare ASVs, eight dominant ASVs corresponding to nematodes, hexapods, arthropods and Sarcoptiform mites, two constant ASVs and two occasional ASVs (Fig. 3).

Fig. 2
figure 2

A Jaccard distances and B Bray-Curtis distances between subsamples. The y axis indicates the distance between subsamples. The blue numbers correspond to the subsamples from site 1, the green numbers correspond to site 2, and the red numbers correspond to site 3

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Fig. 3
figure 3

Olmstead-Tukey diagram for Animalia. The x axis shows the frequency of appearance of the ASVs and the y axis the logarithm of the total abundance of each ASV plus 1. The quadrants of Occasional, Dominant, Rare and Constant ASVs are shown, delimited by the means of each axis

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3.1.2 Protista

The protists' community included 20 ASVs affiliated to the Alveolata superphylum, five for Heterokonta, 17 for Rhizaria and 16 ASVs for Amoebozoa. In total, 29 ASVs could not be assigned to any phylum or superphylum, so they were classified as part of the SAR supergroup or as Protist. Given the high richness of protists, a high heterogeneity was observed in the composition of the communities by subsample. The lowest richness values were presented in the subsamples 8 and 9 from site 3 (trunk), while the highest richness was observed in the subsamples 1 and 4 (Fig. 4). In general, the α diversity of protists in all subsamples was high, particularly in subsamples 5 and 6 from site 2 (A. salvinii), and subsample 1 from site 1 (O. mexicana). Site 3 was the least diverse of the sites, however, the dominance values are very low and the Shannon's index indicated high diversity (Table 2).

Fig. 4
figure 4

Diversity of ASVs corresponding to the kingdom Protista. The x axis shows the three sites with their respective subsamples and the y axis indicates the relative abundance of the ASVs. The red numbers above the bars indicate the total number of ASVs identified in the subsample

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Table 2 Summary of the results produced by the diversity indexes (Simpson, Simpson's Inverse and Shannon) for each subsample (site 1, 1–3; site 2, 4–6 and site 3, 7–9) for Protista
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The community composition was similar among the subsamples of sites 1 and 2, segregating site 3 (Fig. 5A). On the other hand, based on the abundances per subsample, no particular grouping was observed (Fig. 5B). In general, the community presented a balance between the number of dominant and rare ASVs, 34 and 45 respectively; six occasional and two constant ASVs. Dominant ASVs corresponded to the Rhizaria superphylum, whereas rare ASVs were represented by the Alveolata superphylum (Fig. 6).

Fig. 5
figure 5

A Jaccard distances and B Bray-Curtis distances between subsamples. The y axis indicates the distance between subsamples. The blue numbers correspond to the subsamples from site 1, the green numbers correspond to site 2, and the red numbers correspond to site 3

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Fig. 6
figure 6

Olmstead-Tukey Diagram for Protista. The x axis shows the frequency of appearance of the ASVs and the y axis the logarithm of the total abundance of each ASV plus 1. The quadrants of Occasional, Dominant, Rare and Constant ASVs are shown, delimited by the means of each axis

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3.1.3 Plantae

Within the algae, four ASVs were assigned to Chlorophyta, 11 to Rhodophyta and one to Glaucophyta. In general, the algal communities per subsample were dominated by chlorophytes. Most of the rhodophytes were exclusive ASVs of site 3 (trunk), the one with the highest richness (Fig. 7). Subsample 8 from site 3 was the most diverse, followed by subsample 3 from site 1 (O. mexicana). Although site 3 presented the most diverse subsample, it also included the subsample with the highest dominance (subsample 7). In site 2 (A. salvinii), subsamples 4 and 5 showed high dominance (Table 3).

Fig. 7
figure 7

Diversity of ASVs corresponding to the algae of the kingdom Plantae. The x axis shows the three sites with their respective subsamples and the y axis indicates the relative abundance of the ASVs. The red numbers above the bars indicate the total number of ASVs identified in the subsample

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Table 3 Summary of the results produced by the diversity indexes (Simpson, Simpson's Inverse and Shannon) for each subsample (site 1, 1–3; site 2, 4–6 and site 3, 7–9) for Plantae
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According to Jaccard distances, subsamples in site 3 clustered together. In contrast, subsamples from site 1 presented interspersed clustering with those from site 2 (Fig. 8A). Regarding the Bray-Curtis distances by abundance, no grouping pattern was observed since the abundances were highly variable (Fig. 8B). Within the algal community, 9 ASVs were rare and most of these were identified as rhodophytes and the only reported glaucophyte; 4 ASVs were dominant chlorophytes ASVs, and both the occasional (1 ASV) and constant (2 ASVs) ASVs were also rhodophytes (Fig. 9).

Fig. 8
figure 8

A Jaccard distances and B Bray-Curtis distances between subsamples. The y axis indicates the distance between subsamples. The blue numbers correspond to the subsamples from site 1, the green numbers correspond to site 2, and the red numbers correspond to site 3

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Fig. 9
figure 9

Olmstead-Tukey diagram for Plantae. The x axis shows the frequency of appearance of the ASVs and the y axis the logarithm of the total abundance of each ASV plus 1. The quadrants of Occasional, Dominant, Rare and Constant ASVs are shown, delimited by the means of each axis

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3.2 Relationship between environmental variables and diversity

3.2.1 Animalia

The CCA revealed differences in richness and diversity between sites that can be related to the local environmental conditions. The community of site 1 (O. mexicana) is related to a high activity of the enzyme β-glucosidase, low concentration of ammonium and dissolved organic phosphorus. Site 2 (A. salvinii) is characterized by the high activity of phosphomonoesterase, polyphenol oxidase, and concentration of dissolved organic carbon. And finally, at site 3 (trunk) there is a high concentration of ammonium and dissolved organic phosphorus, and the highest pH values (Fig. 10).

Fig. 10
figure 10

Canonical Correspondence Analysis. The x axis represents component 1, the y axis represents component 2, the blue arrows correspond to the environmental variables (GluSid: β-glucosidase enzyme, DOP: dissolved organic phosphorus, NH4: ammonium, pH, PMonEst: phosphomonoesterase enzyme, DOC: dissolved organic carbon and Lac: polyphenol oxidase enzyme), the ASVs are shown in red and the points indicate the position of each site. The CCA1 reflects an 84% explained proportion and the CCA2 a 15%

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3.2.2 Protista

Sites 2 (A. salvinii) and 3 (trunk) were associated with the concentration of dissolved nutrients (C, P and ammonium) as well as the enzymatic activity of polyphenol oxidase and phosphomonoesterase. On the contrary, the communities of site 1 (O. mexicana) were subjected to an environment with limited resources since it was related negatively with all the characterized variables (Fig. 11).

Fig. 11
figure 11

Canonical Correspondence Analysis. The x axis represents component 1, the y axis represents component 2, the blue arrows correspond to the environmental variables (GluSid: β-glucosidase enzyme, DOP: dissolved organic phosphorus, NH4: ammonium, pH, PMonEst: phosphomonoesterase enzyme, DOC: dissolved organic carbon and Lac: polyphenol oxidase enzyme), the ASVs are shown in red and the points indicate the position of each site. The CCA1 reflects a 57% explained proportion and the CCA2 a 42%

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3.2.3 Plantae

Differences were observed in the algae communities associated with the environmental variables. Site 1 (O. mexicana) showed a negative relationship with pH, that is, it was the most acidic site, where Chlorophyta sp. 4 and Rhodophyta sp. 8 occurred as exclusive ASVs. Site 2 (A. salvinii) was linked with polyphenol oxidase and phosphomonoesterase enzymes, as well as dissolved organic carbon concentration. Also, as the CCA showed, site 2 was the only one not having any exclusive ASVs. Finally, site 3 (trunk) was associated with dissolved organic phosphorus and ammonium (Fig. 12).

Fig. 12
figure 12

Canonical Correspondence Analysis. The x axis represents component 1, the y axis represents component 2, the blue arrows correspond to the environmental variables (GluSid: β-glucosidase enzyme, DOP: dissolved organic phosphorus, NH4: ammonium, pH, PMonEst: phosphomonoesterase enzyme, DOC: dissolved organic carbon and Lac: polyphenol oxidase enzyme), the ASVs are shown in red and the points indicate the position of each site. The CCA1 reflects a 77% explained proportion and the CCA2 a 22%

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4 Discussion

To the extent of our knowledge this is the first description of soil microeukaryote diversity in a MCF of Mexico using high-throughput multi-taxa identification from environmental DNA. Former work was based on the direct extraction of the organisms, their morphological description, and identification. This proxy has led to the description of edaphic microfauna in MCF of Jalisco, Chiapas, Oaxaca, Veracruz and Puebla, describing Coleoptera, Diptera, Hemiptera, Chilopoda, Diplopoda, Hymenoptera, Orthoptera and Collembola, as the main orders (Parada-Barrera 1987; Morón-Ríos and Huerta-Lwanga 2006; Bernal-Rojas et al. 2009; De la Rosa and Negrete-Yankelevich 2012; Beltrán-Villanueva et al. 2016). In contrast, our results showed the high representation of Nematoda, the presence of Acari, platyhelminths and unidentified arthropods and an overall diversity of 35 ASVs at the small spatial scale.

Despite algae and protists are known to occur in MCF (e.g. in association with old tree trunks, increasing water-holding capacity and nutrient availability and as photobionts of lichens, Kosecka et al. 2021) reports of these groups in Mexico come mostly from aquatic environments (i.e. freshwater and marine ecosystems), and little is known about their diversity in soils of MCF. For example, from 146 described species of heterotrophic flagellate protists (Rhizaria, Stramenopiles and Excavata) registered for Mexico, only 4 have been isolated from soils and 3 from litter (Mayén-Estrada et al. 2014b); and from the 959 described species of ciliates only 25 have been found in soils (Mayén-Estrada et al. 2014a). Here, we characterized more than 80 ASVs for protists, revealing that soils are potential habitats for a large diversity of protists that needs to be further explored. Remarkably, this approach allowed the detection of potentially non-cultivable protists species that are left out from direct extraction methods. Whereas for soil algal diversity, registers in Oaxaca were limited to the genera Chlamydomonas, Chlorella, Cladophora and Rhizoclonium (Chlorophyta) isolated from Tehuacán-Cuicatlán Valley (Novelo 2012). By overcoming cultivation limitations, we report the occurrence of Rhodophytes and Glaucophytes, paving the way for the exploration of soil diverse environments in MCF and extending the knowledge of the distribution of these groups.

In general, specific clustering patterns in terms of community composition among the study sites were not observed. This reflects a huge heterogeneity in soil at the small spatial scale (1 m). However, we observed differences in terms of the abiotic setting that could contributed to the modeling of microeukaryotic communities in response to variables such as vegetation, litter, and biological activity (Bardgett 2005; Wehner et al. 2016).

Our results evidenced that sites 1 (O. mexicana) and 3 (trunk) were characterized by the dominance of Nematoda 1. This may be related with the high expression of β-glucosidase (produced by carbon decomposer bacteria; Woodward and Wiseman 1982; Baldrian 2006; Sharma et al. 2007), and ammonium (fixed by nitrogen decomposer bacteria and recourse for nitrifying bacteria; Vitousek et al. 2002). According to former work by Leocadio and collaborators (2023), sites 1 and 3 registered the presence of Actinobacteria, Firmicutes and Proteobacteria, confirming the potential availability of resources for opportunistic bacteriophages nematodes. Various studies conclude that bacteriophage nematodes migrate to recourse pulses, explaining their copious occurrence in these sites (Griffiths 1990; Bonkowski 2004; Van der Wal et al. 2012; Shaw et al. 2019). In contrast, site 2 (A. salvinii) reflected the highest diversity values, being related with the enzyme polyphenol oxidase, mainly produced by fungi that decompose lignin (Woodward and Wiseman 1982; Baldrian 2006; Sharma et al. 2007). In accordance to former work demonstrating that the richness and abundance of fungivore nematodes depends on diverse fungal communities (Freckman and Caswell 1985; Zhang et al. 2020), our results confirmed that the presence of fungal-derived exoenzymes are linked to the dominance of this fraction of the microfaunal community.

For protists, the highest diversity values agreed with the presence of A. salvinii (site 2). This diversity pattern may be related with the dissolved carbon concentration and microbial activity (polyphenol oxidase and β-glucosidase), highlighting the role of the rhizospheric nutrients on protists diversity. As rhizospheric carbon compounds serve as resources for microorganisms such as bacteria and fungi, the main prey of protists (Acosta-Mercado and Lynn 2004; Adl and Coleman 2005; Geisen et al. 2018; Samad et al. 2019). This assumption is supported by the copious occurrence of bacterial (Leocadio et al. 2023) and fungal (Velez et al. 2021) diversity in this site.

In terms of algae, the largest diversity was registered in site 3 (trunk), which was associated with high ammonium and dissolved organic phosphorus values, essential for these autotrophic organisms. Both P and N represent determinant factors that positively affect the abundance, biomass, and richness of algae (Lin and Wu 2014; Glaser et al. 2017; Novakovskaya et al. 2022). Also in this site, rhodophytes showed high richness, in accordance with former works reporting that this type of algae proliferates on moist ammonium-rich soils (Thorn and Lynch 2007). Algae typically proliferate on soils devoid of vegetation (Thorn and Lynch 2007). Both sites 1 (O. mexicana) and 2 (A. salvinii), were located at the base of a plant host showing the lowest diversity values. So, it is feasible that this pattern could be linked to negative biotic interactions with plants, microfauna, and protists. Nonetheless this assumption should be further confirmed.

Overall, our results on the large magnitude of soil microeukaryotic diversity (including members of the Plantae, Animalia and Protista) and community composition patterns in relation to the abiotic setting, demonstrate the efficiency of ITS high-throughput multi-taxa identification using environmental DNA for soil microeukaryotic communities. Nonetheless, this methodology remains to be refined to obtain more precise results. For example, by improving the resolution of taxonomic assignments with taxa-specific primers to amplify the scope. Also, as many of these soil organisms have been poorly studied before, reference sequences are not well represented in public repositories. Pairing genetic studies with traditional methodologies of direct extraction of the organisms and morphological identification, may complement our understanding of determining species more than just genetic units (Bogale et al. 2020).

5 Conclusions

A remarkably diverse microeukaryotic community (including members from three kingdoms of life) was registered at the small spatial scale for Mexico in contrast with former reports based on the direct observation of these organisms in soil samples. Our results evidenced the large magnitude of unseen belowground biota and warn on their functional relevance, encouraging future studies aiming to explore individual taxa. Herein, we demonstrated that belowground microeukaryotes are closely interlinked at the community level with further elements of the microbial community (e.g. fungi and bacteria), potentially reinforced by the effect of plants and edaphic variables such as availably carbon and pH. Our data showed that these organisms may be powerful mutual drivers, with both positive and negative feedbacks. So, a combined aboveground-belowground approach to community and ecosystem ecology in MCF is strongly recommended to improve our understanding of ecosystem functioning and conservation.

The ITS metabarcoding proved to be a useful tool to conduct multi-taxa diversity assessments, allowing the identification of alpha and beta diversity patterns overcoming traditional sampling cultivation limitations. Likewise, complementing these results with the analysis of relevant environmental variables, depicted gross community structuring forces, revealing their potential role in the functioning of an ecosystem as diverse and threatened as the MCF. Considering that the main limitation of metabarcoding is the availability of reference sequences in the repositories, it is necessary to enrich public databases with curated and properly identified sequences to broaden the scope of this technique. Despite verifying the effectiveness of the ITS metabarcoding to assess edaphic microeukaryote communities, we acknowledge the importance of conducting complementary studies that implement classic techniques of direct collection, morphological description coupled to the molecular identification of the organisms.