Metabarcoding Inventory of an Arctic Tundra Soil Ecosystem Reveals Highly Endemic Communities

doi:10.21203/rs.3.rs-1957640/v1

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Metabarcoding Inventory of an Arctic Tundra Soil Ecosystem Reveals Highly Endemic Communities

https://doi.org/10.21203/rs.3.rs-1957640/v1

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published 15 Apr, 2023

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Biodiversity surveys of Arctic soil ecosystems are limited. Here, we provide a sequence-based inventory of soil fauna from an Arctic tundra ecosystem near Iqaluktuutiaq (Cambridge Bay), Nunavut. Invertebrate communities were extracted from soil at three sites with vegetation cover and three non-vegetated sites and inventoried using 18S metabarcode sequencing. A total of 361 Amplicon Sequence Variants (ASV) were identified and assigned to the closest matching taxonomic orders, most of which belonged to the Nematoda and Arthropoda. Vegetated soils showed no significantly higher ASV richness relative to non-vegetated soils although they contained a significantly higher diversity of arthropod taxa including insects, mites and springtails. The majority of taxa were found only at a single location and communities were distinct between soils even for sites with vegetation cover, suggesting that belowground species richness in Arctic tundra habitats is highly endemic and heterogeneous. Preserving endemicity is essential for Inuit communities who rely on intact tundra ecosystems for their health and wellbeing.

Arctic tundra ecosystem

Inuit

Soil biodiversity

18S Amplicon Sequence Variants

Arthropoda

Nematoda

The importance of belowground biodiversity for terrestrial ecosystem functioning is well-established (Bardgett and van der Putten 2014; Lefcheck et al. 2015), and much recent focus has been put to investigate drivers of biodiversity loss (Duncan et al. 2015). Our understanding of soil community composition and the individual species’ components has increased rapidly following the application of genomic techniques (Geisen et al. 2019), stable isotope work (Ferlian et al. 2015) and network analyses (Derocles et al. 2018; Ramirez et al. 2018). Yet, specific taxonomic knowledge is still very limited in many belowground systems (Tedersoo et al. 2014) and the biotic diversity of soil ecosystems is often overlooked in conservation policy (Guerra et al. 2021).

The associations between the loss of soil biodiversity and a resulting loss of soil function is well demonstrated (Wagg et al. 2014). Key functions provided by healthy soil ecosystems include nutrient cycling and carbon sequestration (Koltz et al. 2018) in both natural and agricultural systems (Bender et al. 2016). Unfortunately, such functions are diminished as soil ecosystems become degraded, polluted, or over-exploited – leading to a loss of soil biota (Amundson et al. 2015). Human-caused changes in land use and climate directly influence soil food webs and can impact their resistance and resilience to climate change (de Vries et al. 2012, Griffiths and Philippot 2013). Biodiversity loss further directly threatens the ability of these systems to respond to environmental changes, particularly climate extremes (Isbell et al. 2015).

Arctic ecosystems are particularly vulnerable to global and local human activity as they are experiencing some of the most rapid climate warming (Post et al. 2009). Climate change has resulted in risks to Indigenous country food systems through more unpredictable environmental conditions (Statham et al. 2015), a trend that is predicted to continue (IPCC 2022). Many Arctic regions are also seeing a rapid increase in the exploitation of their terrestrial environments for natural resources (Kumpula et al. 2011) and agriculture (Stephen 2018). The potential effects of these further environmental changes on the biota of previously uncultivated soils could be dramatic. The introduction of non-native species as a result of global warming is predicted to further endanger endemic species’ diversity and lead to shifts in species’ ranges (Nielsen and Wall 2013).

Concurrently, Arctic soil ecosystems have a legacy of habitation by humans, including Inuit (Keith et al. 2007), and provide a critical link in supporting traditional country foods such as caribou and muskox. Socio-economic development of high Arctic regions is predicted to intensify the use of natural resources and lead to a degradation of these environments (Ehrich et al. 2019). Links between changes in micro-invertebrate species’ composition and risks to Indigenous food security have already been demonstrated (Kafle et al. 2020). Potential development in the north thus needs to be guided by an understanding of the diversity of soil invertebrates (free-living and parasitic), especially in high Arctic regions (Bach et al. 2020). Inventories of invertebrate diversity also compliment traditional Indigenous knowledge in Arctic communities, and which focus on stewardship of the natural environment (Gadgil et al. 1993).

There is an urgent need to assess the species’ diversity of soil invertebrates in Arctic regions and to identify potential or ongoing risks related to human activities (Pentinsaari et al. 2020). Recent large-scale analysis has shown that nematode densities in sub-Arctic regions are the highest worldwide (van den Hoogen et al. 2019). However, much of this diversity has not been classified taxonomically and the knowledge of the ecological roles of many of these groups is very limited. Currently, invertebrate diversity inventories in the Canadian high Arctic are only available at some long-term research sites (Rich et al. 2013) and for some highly localized sites (Culjak Mathieu 2020). Unfortunately, this may lead to a biased understanding of patterns of biotic diversity across the whole Arctic (Metcalfe et al. 2018), and may not allow local Indigenous communities to identify and respond to threats to their soil ecosystems. However, a framework for generating and curating these data is emerging (Ramirez et al. 2015) and has led to a more comprehensive understanding of climate and vegetation effects (Bastida et al. 2020) as well as distribution patterns of soil invertebrates in the Arctic (Phillips et al. 2019; Gillespie et al. 2020).

Sequencing environmental DNA directly from soils can provide an assessment of belowground diversity for Arctic soil invertebrates (Bik et al. 2012). Further, such genomic inventories can provide critical insights into the complexity of these different ecosystems, such as the presence of predatory or parasitic species’ guilds (Creer et al. 2016). The composition of invertebrates in Arctic soils is known to vary depending on soil type (Hansen et al. 2016) and varying habitats respond differently to environmental change (Coulson et al. 1993). Baseline inventories and subsequent monitoring of soil invertebrates is crucial to understanding the broader consequences of environmental change for soil ecosystems, especially in the Arctic (Høye and Culler 2018).

Here, we extracted and sequenced communities of eukaryotes from soil that was collected from vegetated and non-vegetated sites near the Canadian High Arctic Research Station in Iqaluktuuttiaq (Cambridge Bay), Nunavut. This region has been populated by hunters of fish, seal, muskox and caribou for an estimated 4,000 years (Kitikmeot Heritage Society 2012). Soil inventories therefore provide a taxonomic overview of community structure and diversity for an Arctic tundra system that has been managed under Indigenous stewardship for thousands of years. It contributes to building a comprehensive framework of the biodiversity in this community, relevant to larger vertebrate species and aboveground diversity that provides subsistence for the Inuit people of this region.

Six sampling sites were identified near the Canadian High Arctic Research Station (CHARS) in Cambridge Bay, Nunavut (Fig. 1). A single soil sample was collected from each site in July 2019. Sites were selected based on above-ground plant coverage, with Sites 1–3 consisting of soils with vascular plant coverage and Sites 4–6 consisting of soils from frost-eroded areas with little plant coverage (Fig. 1C/D). The three vegetated sites represented the main plant associations observed, namely: true sedges (Carex spp.) for Site 1; a mixture of other sedge grasses and peat moss at at Site 2; and mixed shrub/sedge grass cover for Site 3. At each site a sample of approximately 250 grams of the topsoil was taken from directly underneath any present vegetation (at the soil surface), and sampled to a depth of 10 cm. Each sample was placed into a sterile plastic bag, homogenized and transported in insulated containers to CHARS prior to freezing and subsequent transportation frozen (< -10°C) to Brigham Young University (BYU) for further analyses

Figure 1

Once at BYU, a modified sugar-flotation protocol was used to extract micro-invertebrates larger than 40µm from 100grams of soil (Freckman and Virginia 1993). DNA was extracted from each sample using a Qiagen DNEasy Blood and Tissue kit (QIAGEN, Germantown, MD), modified by crushing any organisms collected by centrifugation with a sterile pestle before proceeding as per the manufacturer’s instructions. Sequence libraries were constructed using 10µL of the resulting 100µL product, which were amplified using standard Illumina sequencing primers and amplicon primer sequences for the 18S small ribosomal subunit marker (18S) as described in Caporaso et al. (2012). The 18S sequences are recommended by the Earth Microbiome Project (Thompson et al. 2017) for identification of micro-eukaryotes commonly found in soils, and reference databases for this ribosomal region are increasingly found in the literature (Obiol et al. 2020). Sequencing primers and Illumina barcodes were removed before subsequent analysis using cutadapt (Martin 2011). Resulting metabarcoding reads were filtered, trimmed and clustered to Amplicon Sequence Variants (ASVs) using the DADA2 pipeline (Callahan et al. 2016). Reads were quality filtered and truncated at 140 base pairs for the forward and reverse compliment strands to each pair. DADA2 was used for subsequent alpha and beta-diversity analysis and taxonomic assignment to both the SILVA (v 1.32, Quast et al. 2013) and PR2 (v 4, Guillou et al. 2013) databases. Maximum likelihood phylogenetic trees were constructed with the phyloseq package in R (McMurdie and Holmes 2013).

ASVs with poorly resolved taxonomic resolution in the Silva and PR reference databases were further blasted to the NCBI Nucleotide database (Schoch et al. 2020) (May 20th 2021) to be assigned to the taxonomic level for which consensus existed in this database (no other orders showed up as BLAST hits with a score higher than 200). No finer resolution than order-level taxonomy was derived from NCBI BLAST classification.

Following preliminary analyses, we found that Nematoda and Arthropoda were the two most commonly sequenced taxa in each sample by a large margin. Accordingly, the abundance and diversity of Nematoda and Arthropoda were separately considered across the samples to investigate the similarities in invertebrate community composition between soil types.

1. Quality control and data retention

Total reads per sample were 39,286, 41,915, 43,318, 66,907, 59,489 and 41,217 for Sites 1-6, respectively, before trimming, for a total of 292,132 reads. Supplementary Figure 1 shows data loss during filtering and trimming using the DADA2 pipeline (Callahan et al. 2016). Out of 420 resulting Amplicon Sequence Variants (ASVs) sequences 59 were identified as chimeras and removed, making up 3.83% of total reads. After chimera removal the total of reads across all samples was 176,650, for a data loss of just under 40% (see supplementary Figure 1 for data loss in each step).

2. Taxonomic diversity

Based on an initial taxonomic assignment 113 out of 361 total ASVs were identified to the taxonomic Order level in the Silva database, increasing to 147 out of 361 ASVs by combining the Silva and PR databases. Including the NCBI Blast search resolved another 106 ASVs to the Order level, totaling 253 out of 361 ASVs resolved, including all but three of the 50 most common ASVs across all samples. Taxonomic insecurities for the remaining 108 ASVs and the inability to assigned detected taxa to a higher resolution than order level was made difficult by the lack of genomic references for Arctic soil biota. The 18S primers developed for the Earth Microbiome Project are designed to capture a very broad range of eukaryotic diversity but as a result lack the power to resolve species’ taxonomic diversity at a very fine scale (Pawlowski et al. 2012).

The most abundant Phylum in all non-vegetated soils was the Nematoda, while in the vegetated soils Arthropoda made up most of the reads (See supplementary Figure 2). Platyhelminthes were only found in the three vegetated soils, and annelid DNA was recovered from Site 1 only. Across all samples, the other most common phyla after Nematoda and Arthropoda were Rotifera, Platyhelminthes, Ascomycota, Annelida and Tardigrada. Figure 2 presents the results of taxonomic identification across all sites for these seven most abundant phyla, which make up between 90 and 95% of the total reads for each sample (See Supplementary figure 2 for relative abundance). Relative abundances for other phyla are shown in Supplementary figure 3.

2.1 Nematodes

Figure 3 shows the distribution of nematode orders across the different sites. Nematode diversity was not significantly higher between vegetated and non-vegetated soils (one-tailed t-test, p < 0.05). The most common ASV in the entire dataset belonged to the Enoplida and was highly abundant in all three non-vegetated soils while very rare in the soils with vegetation cover. While most enoplids are marine in origin, some members of this genus occur as free-living bacterivorous nematodes in soils (Smythe 2015). The third most common ASV belonged to the Araeolaimida and was highly abundant across all six samples. Araeolaimids are also commonly seen as marine free-living nematodes, with some in the order observed living in soils as free-living bacterivores (Yeates 1988). While these two free-living nematode examples occurred in most soil samples, other nematode ASVs were highly restricted to either soil type or to a single site, such as the most common nematode ASV (belonging to Enoplida), the sixth (belonging to Mononchida) and eighth (belonging to Tylenchida) most common ASVs all being highly abundant in the non-vegetated sites but nearly absent from any of the vegetated soils (see supplementary Table 1).

2.2 Arthropods

Figure 4 shows the distribution of arthropod orders across the different sites. A higher diversity of arthropod ASVs was found in the vegetated soils than in non-vegetated soils (one-tailed t-test, p < 0.05). Sites 4-6 showed a high similarity in arthropod diversity, with a similar composition of collembolan and sarcoptiform arthropods. However, most collembolan ASVs were unique to one or two sample locations, except for the most common collembolan ASV which was present at Sites 1, 4, 5 and 6. Trombidiform mites and any insects such as Diptera were only present in the vegetated soils, which were also the only sites to share the presence of the most common platyhelminth, as well as some other platyhelminths occurring in individual samples. Generally, the vegetated samples showed much higher abundance of arthropod DNA, except for a single collembolan ASV in Sample 5 that made up around 20% of total sample abundance.

3. Inter-site comparison

Seven ASVs were present at all sampled sites, six of which were amongst the 30 most common ASVs. Of the 361 total ASVs, 247 were unique to a single site and a further 31 and 18 ASVs were specific to either vegetated or non-vegetated soils, respectively. The remaining 65 ASVs were not restricted to a single site or soil type. However, many were only present in very low abundances in vegetated soils (as was the case for some of the most common nematode taxa in the non-vegetated soils). The total number of reads that were limited to a single site or soil type was 32.17%, indicating that most taxa were relatively rare across sample sites. Strictly site-specific taxa made up approximately 16% of total read copies, but more than two-thirds of the number of ASVs identified in this study.

Figure 5 shows the alpha diversity distribution across the six sites, including the observed features measure (A) and Shannon and Simpson diversity metrics which account for the proportional abundance of species (Shannon 1948). The sample with a Carex dominated ground cover (Sample 1) consistently showed high diversity compared to the other communities, while Sample 6 (non-vegetated) had the fewest observed features and a low biodiversity index.

Non-metric multidimensional scaling was applied to these six sites to observe their similarities in community composition (Clarke 1993). Patterns of community clustering are shown in Figure 6 for several measures of beta diversity. Two of the vegetated soils (Samples 2 and 3) clustered together in all NMDS plots, and a general distinction between the non-vegetated (Samples 4-6) and the vegetated soils was observed across all plots. The three vegetated soil samples also show higher within-group differences, the Carex-covered soil specifically was less similar to the other two sites, potentially caused by the effects of vegetation cover on community composition. These patterns were consistent between the full biodiversity of the samples and separate comparisons of the nematode and arthropod diversity (Supplementary Figure 4 and 5, respectively).

Figure 7 shows the phylogenetic relationship between the identified nematode and arthropod groups and their presence across the different soils sampled. While monophyletic groups of arthropods can be identified as present only in vegetated soils (all Diptera, Hemiptera and a subset of Collembola) most nematode clades are shown to be present in both soil types.

The composition of the samples we analyzed showed a high degree of endemicity. 247 out of 361 total ASVs identified are strictly endemic (only observed in a single sample). The ecological niches of endemic taxonomic orders can be used as indicators for food web structure (Ferris et al. 2001). Nematodes such as Rhabditida and Tylenchida are shown here to be endemic in non-vegetated soil samples and are likely to be free-living bacterivores (Qing and Bert 2019), while Dorylaimida are seen more frequently as endemic to the three vegetated sites where they comprise free-living or plant and animal parasitic groups (Lee 2002). Most Mononchida are predators and endemic to non-vegetated soils (Ahmad and Jairajpuri 2010), occupying a higher level in the food chain in these ecosystems. Few Trombidiformes were found at the non-vegetated sites while making up roughly one third and one fourth of ASV reads in the vegetated Sites 2 and 3, respectively, suggesting that only vegetated soils host enough prey items for these predatory mites (Seniczak et al. 2020). Sarcoptiform mites were instead found at both vegetated and non-vegetated sites and are mainly expected to have a mycophagous and saprophagous ecology in the Arctic (Young et al. 2012).

The composition of arthropods in these soils show distinct communities exist between the vegetated and non-vegetated sites, with only three out of the 25 total types of Collembola shared between the two soil types, and three out of 12 total mite taxa. Many of the arthropod ASVs were only found at a single site, indicating that some ASVs were potentially associated with the specific plants found at each site, similar to patterns observed for bacterial and fungal communities elsewhere in the Arctic (Bölter 2003). Both phylogenetic trees indicate a high degree of endemicity of nematode and arthropod taxa in these soils, particularly among the rarer observed ASVs in this study. Arthropod species with such patchy distributions are suggested as key indicator species of changing Arctic ecosystems as they are likely to be highly vulnerable to climate changes and invasive species (Hodkinson et al. 2013; Gillespie et al. 2020).

Inuit have inhabited the area around Iqaluktuuttiaq (Cambridge Bay) for centuries and have a long association with, and rich knowledge of, the ecology of their land (Riewe 1991; Keith et al. 2007). Decadal shifts in the macroscopic flora such as “greening” of Victoria Island (Angohiatok et al. 2022) and changes in migratory birds and country food such as caribou and muskox are well known (Cuyler et al. 2020). These ecosystems are warming rapidly and resulting declines in biodiversity and biomass have been demonstrated aboveground (CAFF 2013) - with further declines predicted (Niittynen et al. 2018). However, the belowground components of these ecosystems are generally less well known. The diversity of belowground systems is a driving force behind the above-ground diversity of plants (Frouz 2018) and is therefore of great importance in supporting country food such as muskox and caribou. High Arctic microinvertebrates have also been shown to directly affect this food supply through the transmission of diseases such as brucellosis in wild muskox (Tomaselli et al. 2019) and arthropods such as biting flies can cause behavioral changes in large mammals (Witter et al. 2012). Soil microbiota thus play key roles in Arctic foodwebs supporting the health and wellbeing of Indigenous communities (Pedersen et al. 2020). Understanding the dispersal mechanisms (Coulson et al. 2003) and responses of below-ground fauna to climate and vegetation changes (Joly et al. 2009) can provide Indigenous communities with crucial knowledge to respond to changing environmental conditions.

Our study provides an inventory of belowground invertebrate diversity from near Iqaluktuuttiaq (Cambridge Bay), Nunavut. Many of the ASVs we identified in this study were found only at single sites and point to a high degree of heterogeneity and endemicity in the belowground invertebrate communities of Arctic tundra ecosystems. While some ecological information can be inferred from metabarcoding (Makiola et al. 2020), the incomplete nature of publicly available genetic repositories and biases in geographic and taxonomic representation can reduce confidence in individual species identification and their function in local habitats. Similar and ongoing studies for above-ground invertebrate communities (Pentinsaari et al. 2020) have also highlighted similar issues with existing morphological and molecular taxonomic databases. Our study and ongoing genomic and taxonomic characterization of the eukaryotic soil diversity in the Arctic are first steps in redressing this knowledge gap.

The authors declare no competing interests.

Acknowledgements

Financial and logistic support for this research was provided by Polar Knowledge Canada.

Author Contribution Statement

IH, DW and BA conceived research objectives. JJ and BA designed research approach. BV and IH collected samples. JJ processed samples and analyzed data. JJ wrote the first draft of the manuscript. All authors contributed to the interpretation of data and subsequent drafts.

Data availability

Data used for this study are available at https://www.ncbi.nlm.nih.gov/bioproject/817555

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No competing interests reported.

Sup1Datalossvisualization.png
Supp Fig. 1 Data loss visualization in the DADA2 pipeline. Samples showed roughly equal proportions of data lost during filtering, denoising and chimera removal steps. Truncating and filtering low-quality reads was responsible for a loss of data of up to 40% while subsequent steps resulted in little data loss.
Sup2Nembetadiversity.png
Supp Fig. 2 Absolute number of reads of all orders within the most abundant phyla across samples. Total diversity of phyla was greater than the seven phyla reported on in this study, but most of these phyla had a relative abundance of less than 2%. The number of sequences not assigned to a taxonomic order was very small, except at Site 1 where it reached almost 5%. Sample vegetation: 1) True sedge grasses (Carex spp.), 2) Sedge grass and peat moss, 3) Mixed sedge/shrub cover, 4:6) Non-vegetated soil.
Sup3ArtBetadiversity.png
Supp Fig. 3 Absolute number abundance of the less common phyla not included in Supplementary figure 2. The abundance of these less common phyla was < 10% at each site. Note these phyla also include non-metazoan reads such as fungi. Sample vegetation: 1) True sedge grasses (Carex spp.), 2) Sedge grass and peat moss, 3) Mixed sedge/shrub cover, 4:6) Non-vegetated soil.
Sup4CommonAbundances.png
Supp Fig. 4 Beta diversity community similarities as shown by non-metric multidimensional scaling (NMDS) of the nematode diversity. Four different beta diversity indexes are used: Bray-Curtis distance (A), Unweighted UniFrac distance (B), Weighted UniFrac distance (C), and Jensen-Shannon distance (D). Samples are colored based on soil type (red = vegetated soils, blue = non-vegetated soils). All non-vegetated soil samples show more similarities to each other than they do to the vegetated soils. The three vegetated soil samples also show higher similarity, with the Carex covered soils less similar to the other two sites. Results are thus highly similar to the overall Beta diversity results shown in Figure 3. Sample vegetation: 1) True sedge grasses (Carex spp.), 2) Sedge grass and peat moss, 3) Mixed sedge/shrub cover, 4:6) Non-vegetated soil.
Sup5RareAbundances.png
Supp Fig. 5 Beta diversity community similarities as shown by non-metric multidimensional scaling (NMDS) of the arthropod diversity. Four different beta diversity indexes are used: Bray-Curtis distance (A), Unweighted UniFrac distance (B), Weighted UniFrac distance (C), and Jensen-Shannon distance (D). Samples are colored based on soil type (red = vegetated soils, blue = non-vegetated soils). All non-vegetated soil samples show more similarities to eachother than they do to the vegetated soils. The three vegetated soil samples also show higher similarity, with the Carex covered soils less similar to the other two sites. Results are thus highly similar to the overall Beta diversity results shown in Figure 3. Sample vegetation: 1) True sedge grasses (Carex spp.), 2) Sedge grass and peat moss, 3) Mixed sedge/shrub cover, 4:6) Non-vegetated soil.
SupTable1ASVtableincludingcounts.xlsx
Supp Table 1. Amplicon Sequence Variant (ASV) table including counts. ASV names and sequences are reported along with the combined taxonomic assignment from the Silva and PR databases and Genbank blasts. Counts of each ASV across the samples are also reported, as are the BLAST scores used to fill in taxonomic gaps.

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Metabarcoding Inventory of an Arctic Tundra Soil Ecosystem Reveals Highly Endemic Communities

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