Unexpected species diversity in the understanding of selenium- containing soil invertebrates

doi:10.21203/rs.3.rs-5255864/v1

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Unexpected species diversity in the understanding of selenium- containing soil invertebrates

https://doi.org/10.21203/rs.3.rs-5255864/v1

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Yutangba, situated in Enshi City, Hubei Province, is globally noted its high selenium (Se) content. Soil invertebrates are essential to the functionality and services of terrestrial ecosystems, yet their community composition in this region remains under-explored. This study utilized environmental DNA metabarcoding to investigate the interrelations among environmental factors, soil invertebrate diversity, and community characteristics concerning soil Se content, pH, and moisture content in the region. Environmental factors such as Se concentration, water content, and pH were strongly associated with the alpha and beta diversity of soil invertebrates in Se-rich areas, affecting their distribution and abundance. Among these, Se notably emerges as the primary regulatory factor influencing soil invertebrate diversity. The acidic soil pH, along with moisture, plays a fine-tuning role in regulating species diversity by directly or indirectly influencing the availability and bioavailability of Se, impacting the species richness and community composition. Unexpectedly, certain species, such as the Formicidae (ants, e.g., Odontomachus troglodytes), the Noctuidae (e.g., Diarsia rosaria), and the annelid Haplotaxida Perionyx excavates, exhibit a strong positive association with Se, indicating a high level of Se tolerance among the native species. This novel perspective reveals the complex role of Se in soil ecosystems, emphasizing the necessity of understanding its ecological functions and potential implications for ecosystem health and stability.

Diversity

Selenium

Soil

Invertebrates

Soil invertebrates in Se-rich areas are more tolerant to Se.
Moisture and pH fine-tune soil invertebrate diversity in Se-rich areas.
Some Diptera and Formicidae species diversity increased in Se-rich areas.

The soil microbiome is crucial for ecosystem health, influencing nutrient cycling, organismal growth, and climate regulation through greenhouse gas emissions (Dantas and Sommer, 2014). Soil invertebrates, beyond their role in decomposition, actively break down organic matter via enzymatic processes, thereby affecting both soil and plant dynamics (Joly et al., 2020; Griffiths et al., 2021; Kane et al., 2023). Studies demonstrate that invertebrates independently degrade organic materials without microbial aid, significantly contributing to the global recycling of plant matter (Joly et al., 2020; Kane et al., 2023). Moreover, soil organisms are instrumental in soil formation and development, modifying soil properties, facilitating material translocation, and converting energy (Wall et al., 2008; García-Palacios et al., 2013; Briones, 2018; Griffiths et al., 2021), all of which are vital for ecosystem material cycling.

Studies examining the relationship between soil invertebrates and environmental conditions have identified parent rock type, soil pH, nutrient availability, organic matter content, and climate significantly influence the distribution of soil animal communities (Bird et al., 2000; Ponge et al., 2003; Cassagne et al., 2006). These communities are key indicators of forest health and disturbances. Selenium (Se), an essential trace element for numerous organisms (National Research Council, 1983), exhibits beneficial or toxic effects based on its concentration (Schwarz and Foltz, 1957; See et al., 2006; MacFarquhar et al., 2010; Zhang et al., 2021). Soil pH significantly affects animal diversity and the composition of functional communities, with distinct decomposer species thriving in either acidic or alkaline environments (Schaefer, 1990; Salmon et al., 2006; Pollierer et al., 2021). This indicates that regional abiotic factors, such as soil pH, exert a greater influence on decomposer communities than local biotic factors. Soil pH, determined by parent rock and stand type, is fundamental to the availability and structure of resources within soil food webs (Ruess et al., 1996; Lauber et al., 2008; Rousk et al., 2010). Consequently, soil Se content and pH are primary determinants of soil organism distribution.

Se distribution exhibits significant variability across the globe, with concentrations reaching up to 1000 mg/kg in certain Se-rich regions, juxtaposed with areas where it is nearly nonexistent (Hartikainen, 2005). Outside these enriched zones, Se is also found in uranium and phosphate ore deposits. Yutangba, recognized worldwide for its high Se content, is notable for containing the only known independent Se deposit, located within black rock series (Yao et al., 2002; Zhu et al., 2000; Wen et al., 2007). Despite its small area of merely 0.01 km², Yutangba demonstrates considerable heterogeneity in soil Se distribution, predominantly shaped by topographical variations and soil particle size (Zhu et al., 1998). Soil organisms significantly influence soil formation, yet the effect of the region's uneven Se distribution on their distribution is not well understood. Soil invertebrates near the ancient Se mine have likely evolved adaptive mechanisms due to extended exposure (De La Riva and Trumble, 2016). Consequently, it is hypothesized that Se-rich environments support greater soil invertebrate diversity, with factors such as Se content and soil pH playing substantial roles in shaping this diversity.

Environmental DNA (eDNA) technology, utilizing molecular sequence analysis of genetic material in environmental samples, allows for efficient identification and monitoring of biological species through comparison with reference species databases. This technology marks a substantial advancement in biodiversity science since the early 21st century (Wang et al. 2019, Zhang 2019). This study employed eDNA technology to compile foundational data on the soil animal community in the region. By analyzing the relationships between soil invertebrate diversity indicators and soil properties such as Se content, the research established a framework for understanding the association between soil Se levels and soil fauna and monitoring soil ecological health. The results offer valuable insights for future research into the characteristics and evolutionary dynamics of soil animal diversity in the area.

2.1 Sample collection and the separation of soil organisms

The designated soil sampling route at Yutangba, Enshi City, Hubei Province, as illustrated in Fig. 1, targeted the central zone of a distinct Se reserve, beginning at sites K and A. The route traced two watercourses, merging at site G and then extending downstream. Eleven sampling locations were established, with three subplots at each site arranged in S-shaped or daisy-shaped patterns to ensure randomness and equitable volume, representing a composite of multiple points. Soil sampling was conducted thrice at each of the 11 sites in July, September, and November 2015, yielding 99 soil specimens. Post-sampling, the soil specimens were transported to the laboratory, separated using modified Tullgren funnels, and subjected to a 48-hour drying period. The dried samples were then categorized and preserved in an 85% ethanol solution. For environmental DNA analysis, 33 composite samples were prepared from the corresponding sample points.

2.2 Determination of moisture content

Upon returning to the laboratory, labeled soil samples were collected. Each sample was weighed in a clean culture dish, recording the mass as M_dish. After adding the soil to the dish, the combined mass was documented as M_{dish+soil sample}. Samples were then dried to a constant weight in a temperature-controlled drying oven, and the mass was noted as M_dish+_dried. Moisture content of the soil sample was calculated using the formula: Moisture content (%) = [(Mdish + soil sample - Mdish + dried) / (Mdish + dried - Mdish)] × 100%.

2.3 Determination of selenium content

Determining soil Se concentration adhered to the Chinese National Standard GB 5009.93—2017 (The Ministry of Health of the People's Republic of China, 2017). Dried soil samples were ground and sieved through a 200-mesh screen before being stored in sealed plastic bags. A precise 0.05 g portion of each soil sample was weighed and placed in a digestion tube. To enable digestion, 8 mL of concentrated nitric acid, 2 mL of concentrated hydrochloric acid, and 2 mL of hydrogen peroxide were added to each tube, with the samples soaking for 24 hours. The digestion process entailed a gradual temperature increase in a digestion furnace until the solution achieved clarity and transparency. The solution volume was then reduced, and hydrochloric acid was added to maintain clarity. The digested solutions containing Se were transferred to volumetric flasks and diluted to a known volume with ultrapure water. Analysis was performed using an AFS-922 dual-channel atomic fluorescence spectrometer, as detailed in Appendix A. The Se content, expressed as micrograms per gram (µg/g), was calculated by comparing the initial weight of the soil samples with the final volume of the digested solutions for subsequent analysis.

2.4 Determination of pH

Post moisture content assessment, the remaining soil samples were exposed to natural air drying and subsequently sieved through a 1 mm mesh. A 20-gram portion of the sieved, air-dried soil was weighed and transferred into a 120 ml wide-mouth bottle with a cover. Then, 100 ml of CO2-free distilled water was added, maintaining a soil-to-water ratio of 1:5. The mixture was blended for 2 minutes and allowed to settle for 30 minutes. The pH of the resulting solution was measured with a pH meter, and the data were recorded.

2.5 DNA extraction and PCR amplification

DNA from soil eukaryotic invertebrates was extracted from 33 samples using the E.Z.N.A.® Soil DNA Kit (Omega Biotek, Norcross, GA, U.S.), adhering to the manufacturer's protocols. The COI gene underwent PCR amplification under these conditions: initial denaturation at 95°C for 2 minutes, followed by 25 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, with a final extension at 72°C for 5 minutes. Two oligonucleotide primers, mlCOlintF (5’-GGWACWGGWTGAACWGTWTAYCCYCC-3’) and jgHCO2198 (5’-TAIACYTCIGGRTGICCRAARAAYCA-3’), were utilized. Each PCR was performed in triplicate in a 20 µL volume containing 4 µL of 5 × FastPfu Buffer, 2 µL of 2.5 mM dNTP mix, 0.8 µL of each primer at 5 µM, 0.4 µL of FastPfu Polymerase, and 10 ng of DNA template. DNA fragments were isolated from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.), following the manufacturer's guidelines precisely.

2.6 DNA sequencing and data processing

Concentrations of the PCR products were measured using the Qubit® 3.0 Fluorometer (Life Invitrogen). Equal molar amounts of the twenty-four amplicons, tagged with a specific barcode, were combined to create a composite sample. This composite DNA was utilized to construct a paired-end library suitable for Illumina sequencing platforms, following the provided protocol for genomic DNA library preparation. The library underwent paired-end sequencing (2 × 250 bp) on an Illumina MiSeq system (provided by Shanghai BIOZERON Co., Ltd.), adhering to the standard sequencing protocols.

Raw fastq files were processed with custom Perl scripts to demultiplex data using unique barcode sequences for each sample. The demultiplexing adhered to stringent criteria: (i) Reads of 250 base pairs were truncated if a sliding window of 10 bases had an average quality score below 20, and truncated reads shorter than 50 base pairs were discarded. (ii) Reads with exact barcode matches, more than 2 nucleotide mismatches in primer sequences, or ambiguous nucleotide codes were excluded. (iii) Overlapping sequences were assembled only if the overlap exceeded 10 base pairs, and reads failing to meet this overlap requirement were rejected.

2.7 Statistical analysis

Statistical analyses and visualizations were conducted using R (version 4.3.0) and the ggplot2 package (Wickham, 2016), except where noted. Data from eleven sample site groups were assessed for normality. Normally distributed data were analyzed using ANOVA and the least significant difference (LSD) multiple comparison test. Non-normally distributed data were evaluated using the Kruskal-Wallis (KW) test and the Wilcoxon test. Statistical significance was set at P < 0.05.

Species richness estimation and diversity comparison among samples employed rarefaction and extrapolation methods. Invertebrate alpha diversity and abundance were assessed using the Chao1 estimator, Abundance-based Coverage Estimator (ACE), and observed species index, calculated via R scripts. Relationships between alpha indices and environmental variables were examined through Canonical Correlation Analysis (CCorA). Beta diversity of invertebrates was analyzed using PCoA based on Bray-Curtis distances and PERMANOVA, utilizing the vegan package in R (Oksanen, 2002). Soil community structure differences were investigated with a nonparametric multivariate analysis of variance (Adonis).

Canonical correspondence analysis (CCA) assessed the relationships between environmental variables and species/operational taxonomic units (OTUs). The envfit test within the R package vegan subsequently identified environmental factors significantly impacting species distribution. Additionally, Mantel correlograms with 9,999 permutations tested the significance of correlation trends in concentration changes. The Mantel test further examined correlations between species and environmental variables, including Se, H₂O, and pH, using Spearman's rank correlation, with significance determined by 9,999 permutations. Significant correlations were defined by Spearman’s rho values exceeding 0.2 and P-values below 0.05. Data visualization was performed in Cytoscape (http://www.cytoscape.org) to elucidate the relationships between species and environmental factors.

Canonical correspondence analysis (CCA) assessed the relationships between environmental variables and species/operational taxonomic units (OTUs). Following CCA, the envfit test in the R package vegan identified specific environmental factors significantly impacting species distribution. Additionally, Mantel correlograms with 9,999 permutations were utilized for significance testing to assess the correlation trends of concentration changes. Moreover, the Mantel test was conducted to explore correlations between species and environmental variables including Se, H₂O, and pH, using Spearman's rank correlation, with significance assessed through 9999 permutations. A Spearman’s rank correlation coefficient (Spearman’s rho) greater than 0.6 and a significance level (P) less than 0.05 were considered significant.

3.1 Analysis of environmental factors

Samples were collected from 11 locations adjacent to the stream, where two streams converge downstream of site G and upstream of site F. Site A was positioned at the selenium cave, while site K was located near selenium mining waste (Fig. 1).

To assess environmental factor variations, month and site relationships were analyzed. The results showed no significant interactions for Se (F _{(10, 11)} = 1.75, P = 0.19), H₂O (F _{(10, 11)} = 0.39, P = 0.93), and pH (F _{(10, 11)} = 1.42, P = 0.29). Then, variations in pH, H₂O, and Se were assessed individually across months and sites. Results showed no significant differences among the months (Fig. 2a-c, all P > 0.05), but significant site-specific differences were observed (Fig. 2d-f, all P < 0.05, Appendix B). All sites exhibited acidic pH values below 7.0, with the highest at 6.4, suggesting a generally weakly acidic soil environment. Sites with higher Se concentrations had more acidic pH levels (Fig. 2d-e). Soil water content ranged from 27.6–48.7%, peaking at site C with 62.56% and the lowest at site A. Despite site A's low water content, it had a high Se concentration of 254.37 µg/g, indicating that both Se concentration and water content influence soil organism diversity.

3.2 Taxa composition and abundance analysis

In July, September, and November 2015, 33 samples were collected from 11 sites. COI amplification sequencing produced 1,478,635 circular consensus sequencing (CCS) sequences with an average length of 312.23 bp. Optimized sequences totaling 1,468,280, ranging from 301 to 350 bp, represented 99.30% of the total, ensuring annotation accuracy (Appendix C).

Analysis of taxonomic composition and differential relative abundance of soil invertebrates revealed distinct patterns across various sites (Fig. 3). The phyla Arthropoda and Annelida consistently exhibited high abundances. Within these groups, Diptera (including Culicidae and Tachinidae) in the Insecta class and Haplotaxida (including Henicopidae and Megascolecidae) in the Chilopoda class were particularly prominent. Additionally, sites C, H, and I, which recorded the highest humidity levels, showed a greater abundance of Anopheles mosquitoes, indicating their preference for aquatic environments. Variations in environmental factors, such as Se concentration and water content, are linked to the distribution and abundance patterns of soil invertebrates.

3.3 Alpha diversity signatures

A comparative analysis was conducted to investigate the impact of various sites and environmental factors on alpha diversity. Despite site B having lower Se concentration but higher pH and water content compared to site A (Fig. 2d-f), alpha diversity was relatively higher at site B (Fig. 4a-d). This suggests that pH and water content, among other environmental factors, significantly influence soil invertebrate alpha diversity, shaping biodiversity.

Given the spatial distribution of sites along the stream, interrelated data from these locations exhibit notable correlations. Analysis of alpha indices across different sites revealed significant inter-site correlations (Fig. 4e). To further explore the relationships between alpha diversity indices and environmental variables, including Se, pH, and H₂O, CCorA was performed. This analysis indicated a substantial correlation between environmental factors and alpha diversity at various sampling sites. Specifically, CCorA results demonstrated a strong correlation between alpha diversity indices and environmental variables (canonical correlation coefficient = 0.85, P = 0.014; Wilks' statistic = 0.20, df = 24) (Fig. 4e). These data highlight the robust association between environmental factors and alpha diversity.

3.4 Beta diversity

PCoA of OTU data on species diversity indicated that three axes accounted for 21.5% of the variance (Fig. 5a-b). The composition of invertebrates in the Yutangbae area was significantly different among the various sites (PERMANOVA R² = 0.37, P = 0.001), while no significant differences were observed among different months (PERMANOVA R² = 0.03, P = 0.23). It suggests that spatial variations have a greater impact on soil invertebrate diversity than temporal variations. The combined CCA and envfit analysis identified significant correlations between environmental factors such as Se, water content, and pH with variations in species composition within soil animal communities (Fig. 5c-d). This analysis highlighted the substantial impact of Se and pH on the structure and dynamics of these communities. R² values (0.31, 0.60, and 0.68) revealed that Se, H₂O, and pH accounted for 31%, 60%, and 68% of the observed variation, respectively, with low P-values of 0.005, 0.001, and 0.001, confirming statistical significance. These results highlight the essential roles of Se, H₂O, and pH in determining species composition. The strong association between environmental factors and community structure highlights the necessity of considering these variables in ecological studies and management.

Analysis of environmental variables pH, H₂O, and Se revealed no significant correlation between Se and the other two factors (Fig. 6a). However, a significant positive correlation was identified between H₂O and pH (rho = 0.47, P < 0.01) (Fig. 6a). The stability of pH across different sample sites may be due to their proximity to water sources, where pH remains relatively constant. A Mantel test was performed to evaluate the relationship between species beta diversity and specific environmental factors, revealing a significant positive correlation between species beta diversity and sample sites (P < 0.05) (Fig. 6a). Additionally, H₂O significantly influenced site A (rho = 0.23, P = 0.046). Sites A, J, and K, characterized by high species diversity and elevated Se concentrations (Fig. 6a, Fig. 2d), suggest that Se significantly enhances species diversity at Se-enriched areas.

To analyze the relationship between species diversity and changes in environmental factors such as alpha diversity, Se content, H₂O content, or pH, we used Mantel correlograms with 9999 permutations for significance tests (Fig. 6b). Results indicate a significant negative correlation between species diversity and Se content in Se-rich areas with low soil Se (r = -0.52, P = 0.008), implying a necessary Se threshold for soil invertebrate survival. Unexpectedly, in regions with high Se concentrations, a significant positive correlation emerges between species diversity and Se concentration (r = 0.68, P = 8E-4), indicating species tolerance to elevated Se levels. Furthermore, significant positive correlations were observed with high alpha diversity (r = 0.39, P = 0.036), high water content (r = 0.78, P = 5E-04), and high pH (r = 0.42, P = 7E-4), suggesting that these environmental factors were also associated with species beta diversity.

3.5 Relationships between environmental variables and soil invertebrates

Correlation analysis between soil invertebrates and environmental factors (Fig. 7, Appendix D) identified significant associations for 83 distinct species with Se concentration, soil moisture content, and pH. Insects demonstrated notable correlations with environmental factors, with 74% of species belonging to the order Insecta. The majority of soil organisms exhibited a strong and statistically significant relationship with Se concentration, as 67% of the correlation coefficients (rho) exceeded 0.5. The Formicidae family (ants), including species such as Odontomachus troglodytes, Lasius emarginatus, and Lasius niger, along with the Noctuidae family, such as Diarsia rosaria, and Haplotaxida, specifically Perionyx excavates, showed a high correlation with Se. In contrast, the aquatic larvae of Diptera, such as Drosophila littoralis, were strongly associated with moisture content. In addition, Coleoptera (beetles) exhibited a notable correlation with soil pH. The number of species associated with Se significantly exceeded those linked to water and pH, indicating that Se distribution and concentration had a more substantial impact on insect diversity and distribution. Furthermore, the correlation coefficient (rho) between species related to Se and water is typically higher than that between species related to pH. This suggests that Se and water may have a more direct and significant impact on insects, particularly during the larval stage when they are more sensitive to environmental changes. Although five species exhibited significant correlations with both Se concentration and pH, the correlation coefficients (rho) with Se were consistently higher than those with pH. This indicates that the abundance of these soil invertebrates is more significantly influenced by Se concentration.

Robinson et al. (2018) investigated the impact of soil temperature on the structure and diversity of invertebrate communities. Despite fluctuations in local moisture content and temperature regimes across different months, species diversity remained unaffected, suggesting that environmental conditions in Se-rich areas are relatively stable over time. Therefore, future research should focus on how spatial variations in these factors influence soil invertebrate diversity, rather than temporal changes.

Soil pH influences Se adsorption and desorption, thereby controlling its bioavailability. In acidic soils, Se predominantly exists as tetravalent selenite, forming stable complexes with iron and manganese oxides (Guo, 2012), which sequesters Se in the soil and maintains high Se levels. This study identified a correlation between soil moisture and pH, both affecting the distribution of soil invertebrate communities (Sylvain et al., 2014; Kökdener and Şahin Yurtgan, 2022; Rai et al., 2022; Wu et al., 2023). Soil pH regulates the adsorption and desorption of Se, affecting its bioavailability. In acidic soil, Se predominantly exists as tetravalent selenite, which forms stable complexes or precipitates with iron and manganese oxides (Guo, 2012), thus sequestering Se and maintaining high Se levels. Soil samples from Yutangba, generally exhibiting a weakly acidic environment, indicate that Se is primarily in its tetravalent form, potentially impacting soil health (Guo, 2012). High Se concentrations are often associated with more acidic pH levels, suggesting that pH significantly influences Se bioavailability in the Yutangba region. However, the content of Se showed no significant correlation with factors such as pH and H₂O (Fig. 6a), even though pH and H₂O were significantly correlated. These findings suggest that Se significantly influences species diversity in Se-rich environments, enhancing beta diversity at high concentrations while reducing it at low concentrations, with both moisture content and pH serving as fine-tuning factors in the dynamics of Se bioavailability and ecological processes (Sylvain et al., 2014; Kökdener and Şahin Yurtgan, 2022).

It is well established that selenium is an essential element for animal survival, and its deficiency can negatively impact species viability (So et al., 2023). This study not only confirms the importance of selenium but also unexpectedly reveals that Se enhances species diversity at high concentrations. This finding challenges the conventional understanding that high selenium concentrations typically inhibit growth and potentially affect survival (Xiao et al., 2018; Yue et al., 2021). The pivotal role of earthworms in soil ecosystems (Butenschoen et al., 2009; Singh et al., 2016) is further underscored by the significant positive correlation observed between Se concentration and the abundance of earthworm species, such as P. excavates (Fig. 6b and Appendix D). Additionally, earthworms can reduce local heavy metal content (Singh et al., 2016), which may indirectly contribute to Se tolerance. Previous research has highlighted the importance of moisture and pH for earthworm distribution (Singh et al., 2020). This study, however, found no significant correlation between the beta diversity of P. excavatus and moisture or pH levels, suggesting that selenium concentration is the predominant factor influencing earthworm distribution in Se-enriched areas.

Aligned with the finding that insects exhibit a certain level of tolerance to Se and have developed mechanisms to control its absorption into their tissues (Lalitha et al., 1994). In Yutangba’s soil, Se was widely enriched, providing insects with additional nutrients (Guo, 2012). This nutrient benefits plant absorption and utilization (Guo, 2012; Wang et al., 2022a; Chen et al., 2023), as evidenced in Cardamine hupingshanesis (Brassicaceae), a plant highly tolerant to Se (Yuan et al., 2013). It also influences the distribution of soil microbial populations, including Se-tolerant bacteria (Wang et al., 2022b, 2023; Yuan et al., 2023; Zang et al., 2023). Consequently, these soil nutrients may directly and indirectly impact soil invertebrate biodiversity (Yang et al., 2022). Despite difference in absorption efficiency between selenite and selenate, their Se bioavailability remains roughly equivalent due to the metabolic conversion of selenate to selenite and substantial pre-metabolic excretion of excess selenate (Burk and Hill, 2015; Vickerman et al., 2004). Additionally, parasitic insects expose larvae directly to organic selenium rather than selenate, thereby circumventing selenium toxicity (Vickerman et al., 2004). This mechanism may contribute to the higher Se tolerance observed in native species (De La Riva and Trumble, 2016). Due to the relatively lower concentration of water-soluble Se in Enshi’s aquatic environments (Shao, 2020), a Se gradient forms in small streams, enabling species to select optimal Se levels for growth and reproduction within the high Se soil conditions. Furthermore, certain native species have developed unique adaptabilities to selenium. For example, the abundance of ants like O. troglodytes, L. emarginatus, and L. niger is positively correlated with Se concentrations, which is consistent with research indicating high Se tolerance in ants relative to other native species (De La Riva and Trumble, 2016). This correlation implies that extended exposure to Se-rich environments, such as mine caves, may have led to greater Se tolerance in these invertebrates compared to those in low Se areas. Long-term exposure to Se appears to enhance Se tolerance in soil invertebrates in such areas, supporting our hypothesis.

In summary, environmental factors intricately impact soil health and biodiversity. In Se-enriched habitats, Se significantly shapes species diversity, with its bioavailability being further modulated by moisture and pH levels, thus finely tuning the ecological dynamics in these regions. Notably, the sampled site, a natural Se mine, indicates that certain native species have likely adapted to high-Se environments. Balanced Se concentrations in soil enhance species diversity. This study elucidates the extensive implications of Se on invertebrate biodiversity and provides valuable insights into the intricate interplay between Se and soil ecosystem health.

This study conducted at Yutangba, within the Se-rich Enshi region, examined the correlation between soil invertebrate diversity and environmental variables, with a specific emphasis on Se, moisture, and pH. The findings revealed a substantial impact of these factors on the alpha diversity of soil invertebrates. High alpha diversity areas demonstrated a close association between beta diversity and soil moisture as well as Se content. In contrast, regions with fluctuating pH and moisture levels exhibited beta diversity that was independent of Se concentrations. These data suggest that in Se-rich areas, Se primarily influences beta diversity, whereas in low-Se conditions, moisture content also significantly regulates beta diversity.

Author Contribution

Conceptualization, Y.F., B.M; methodology, B.M., X.-L.F.; software, B.M., X.-L.F.; formal analysis, B.M., X.-L.F.; investigation, Y.F.; resources, Y.F.; writing—original draft preparation, Y.F., B.M., H.-L.L.; writing—review and editing, Y.F., Y.-L.X.; visualization, B.M.; supervision, Y.F.; project administration, Y.F.; funding acquisition, Y.F.; All authors have read and agreed to the published version of the manuscript.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 32070483, 31460572), Natural Science Foundation of Hubei Province (Grant No. 2020CFB757), Scientific Research Starting Foundation for Ph.D. of Huanggang Normal University (Grant No. 2020010, 2042024378), Scientific Research Project of Education Department of Hubei Province (Grant No. Q20232901), Huanggang Normal University Major Project Incubation Plan-Funded Program (Grant No. 204202315204).

Data availability

The data obtained in this study are available from the NCBI under the GenBank number PRJNA1055709. All other data are available in this text and in the Appendixes.

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No competing interests reported.

Supplementarymaterial.docx
Appendixes The supplementary materials include the following Appendixes: Appendix A: Atomic fluorescence spectrometer; Appendix B Analysis data of environmental factors at different sites;Appendix C Length distribution of valid sequences. Appendix D: Correlation coefficients and significance tests obtained from the Mantel test between different species and environmental factors.

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Unexpected species diversity in the understanding of selenium- containing soil invertebrates

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Abstract

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1 Introduction

2 Materials and methods

2.1 Sample collection and the separation of soil organisms

2.2 Determination of moisture content

2.3 Determination of selenium content

2.4 Determination of pH

2.5 DNA extraction and PCR amplification

2.6 DNA sequencing and data processing

2.7 Statistical analysis

3 Results

3.1 Analysis of environmental factors

3.2 Taxa composition and abundance analysis

3.3 Alpha diversity signatures

3.4 Beta diversity

3.5 Relationships between environmental variables and soil invertebrates

4 Discussion

5 Conclusions

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Author Contribution

References

Additional Declarations

Supplementary Files

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