Next Article in Journal
Resident Perceptions of Environment and Economic Impacts of Tourism in Fiji
Previous Article in Journal
Study on Splitting Damage Characteristics of a Rock–Shotcrete Interface Subjected to Corrosive Water
Previous Article in Special Issue
Advances in Groundwater Quality and Protection for Sustainability
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 9
10.3390/su14094988
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hydroecology of Argyroneta aquatica’s Habitat in Hantangang River Geopark, South Korea

by
Jinah Moon
,
Heejung Kim
* and
Han-Sun Ryu
Department of Geology, Kangwon National University, Chuncheon 24341, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 4988; https://doi.org/10.3390/su14094988
Submission received: 26 March 2022 / Revised: 15 April 2022 / Accepted: 19 April 2022 / Published: 21 April 2022
(This article belongs to the Special Issue Advances in Groundwater Quality and Protection for Sustainability)
Browse Figures
Versions Notes

Abstract

:
The water spider (Argyroneta aquatic) is the only known spider to live a fully aquatic life. Therefore, it has been the subject of a series of studies on various aspects of its unique biology such as its reproductive behavior, sexual dimorphism, physiology, genetics, and silk. However, there have been relatively few studies on the hydroecology of where water spiders live. The water spider habitat in Eundae-ri, Yeoncheon is the only habitat for A. aquatica, a globally rare species, in South Korea. In this region, the water level of the wetland is automatically adjusted to groundwater owing to continued drying. Here, the surface water, wetland, and groundwater near the A. aquatica habitat were studied using hydrochemical, microbiological, and correlation analyses. The hydrochemical properties—water temperature, pH, electrical conductivity, dissolved oxygen (DO), oxidation reduction potential, and turbidity—of the surface water and wetland were similar. The Piper diagrams revealed that the wetlands, surface water, and most of the groundwater portrayed Ca-HCO3-type properties, whereas only areas where the water level of the wetland was controlled displayed Na-HCO3-type properties. Furthermore, the NO3 content was too low to be detected in the wetland, indicating clean and non-polluted water conditions; additionally, heavier oxygen-hydrogen isotopes were observed because these regions were climatically affected by the wetland. The dominant bacteria were Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Nitrospirae. The correlation analysis revealed that the major environmental control factors of the A. aquatica habitat were DO, temperature, and pH, and the related bacteria were Cyanobacteria, Actinobacteria, and Verrucomicrobia.

1. Introduction

Spiders are distributed in all types of environment and adapted to various environmental conditions. However, most species prefer a specific climate zone, and the species also varies depending on the type and availability of food [1]. Argyroneta aquatica (Clerck, 1757) from the family Cybaeidae is a rare spider species; it is the only existing species of the genus Argyroneta worldwide. Although it lives in an aquatic habitat, it cannot breathe in water, because unlike aquatic insects, they do not have gills. They form air bubbles, build spider webs and bubble nests in the water, and spend most of their lives in the water (Figure 1a,b) [2,3,4]. The species has been reported in running waters only occasionally [5]. According to Bristowe (1958) [3], shallow areas of the ponds are generally preferred by A. aquatica, especially for laying its cocoons. Although the species can exchange enough oxygen from the water even at high temperatures [6], it prefers room temperature (15–25 °C) [7,8,9]. It has been observed that it can survive in moderate acid and unoxygenated waters, where other predators cannot [5,7,8].
Argyroneta aquatica is distributed across Europe, Siberia, central Asia, Japan, China, and South Korea (Figure 1c). Generally, A. aquatica thrives in oligotrophic/eutrophic ponds, marshes and swamps, resurgences, small lakes, and slow-moving streams [5,7,8,10,11,12,13,14,15].
Generally, inland wetlands can control floods and promote groundwater recharge by absorbing and storing excess water from heavy rains. The species that live in wetlands can become locally extinct rapidly, as wetlands experience frequent dynamic changes. Therefore, compared to land species, organisms such as A. aquatica are more likely to disappear. The destruction of wetland habitats has led to a considerable loss of A. aquatica populations [16]. In general, wetlands are developed and cultivated by a combination of factors. The top priority is climatic and hydraulic conditions that can control the supply of water to wetlands; the other crucial factor is maintaining a watershed environment where moisture can remain stagnant. Because many factors act in combination in wetlands, they are characterized by high sensitivity to even minor changes in the environmental conditions of watersheds [17,18,19,20].
In South Korea, A. aquatica was first discovered in a shallow water pool created from the tracks of vehicles formed in a clay layer of a lava plateau in Jeongok in 1994 [21]. To date, the species could be found in a wetland that covers the Eundae-ri, Jeongok-eup, Yeoncheon-gun, and Gyeonggi-do areas; therefore, the wetland has been designated and protected as a natural monument (No. 412) [2,22]. Notably, because this wetland has a small area and the population of A. aquatica is small, the species is considered to be very vulnerable to environmental changes [23]. The A. aquatica habitat that has been preserved until now comprises of a shallow wetland, with a water depth of 20–30 cm. Currently, the wetland habitat is being threatened by high temperatures and dryness owing to rapid climate change and anthropogenic interferences. To date, studies on the habitat of A. aquatica in Eundae-ri have focused on the geographical and vegetative characteristics of the region [21,24,25].
Argyroneta aquatica spawns three times in May (in the adult stage) and from June to October [8]. Therefore, we examined the A. aquatica habitat and the hydraulic and microbiological interconnectivity of the A. aquatica habitat in May to July, the early part of its most prosperous period in a year (May to October). Thus, our study can provide the basic data required to study the species and their habitat from the perspective of non-geological science (natural ecology) and the topographical environment of the habitat, which covers the Hantangang River Geopark that was certified as a United Nations Educational, Scientific, and Cultural Organization (UNESCO) global geopark on 7 July 2020. UNESCO global geoparks are certified by the organization for their aesthetic value, scientific importance, and archaeological, cultural, ecological, and historical values. Therefore, we selected the wetland in Eundae-ri (located in the Hantangang River Geopark), Yeoncheon-gun in South Korea, as our study area. We selected this area because it had the potential to be an ideal geological site for our study, in terms of ecology, geology, and topography.
The aim of our study was to clearly portray the effects of the changes in the surface water and groundwater on wetlands, while focusing on A. aquatica, which is a vulnerable and rare species. Notably, our study findings can help support other studies to understand and analyze the correlation between different ecosystems, habitats, and environments, especially with respect to specific species populations.

2. Study Area

Our study area was an A. aquatica habitat (wetland) in Eundae-ri, Jeongok-eup, Yeoncheon-gun, Gyeonggi-do (South Korea), which has been designated and protected as a Natural Monument no. 412 since 1999 (Figure 2). The wetland, formed in a lava plateau (altitude of 60–70 m), is located between the Hantan River and Chatan Stream. The rocks in this lava plateau comprise of the Yeoncheon Group’s Misan Formation of the Paleozoic period, East of the Hantan River. To the west of the river, near Mount Gunja and surrounded by the Hantan and Imjin rivers, a lava plateau is located that is mostly composed of quaternary basalt. The surface of the lava plateau is covered with quaternary alluvial materials, but the base is made of basalt.
The average temperature of the study area over the last 10 years was 11.7 °C, and the average precipitation was 1232.3 mm (Figure 3).
With respect to the global distribution of the species, the average temperature and precipitation over the last 10 years was 9.3 °C and 1466.4 mm in the United Kingdom, 10 °C and 1306.6 mm in Ireland, 10.8 °C and 913.5 mm in Netherland, 10 °C and 688.9 mm in Germany, 7 °C and 8.7 mm in Norway, 16.5 °C and 1658.9 mm in Japan, and 0.9 °C and 473 mm in Australia, respectively (Table 1). Notably, Ireland and the UK have an A. aquatica habitat environment similar to South Korea.

3. Materials and Methods

3.1. Field Survey and Hydrochemical Analysis Method

In this study, a total of nine sites were selected, including three sites in the A. aquatica habitat (wetland) in Eundae-ri, two surface water sites (Chatan Stream, Hantan River), and four groundwater well sites withing a radius of 500 m of the habitat, to examine the hydrochemical and microbiological connectivity of the groundwater, wetland, and surface water in the study area; the sampling was conducted from May 2021 to July 2021. On-site water quality surveys were conducted three times for each site for hydrochemical and microbiological analyses. In May, the EDG4 site could not be sampled because of well failure, and in July, the EDW3 could not be measured because the wetland dried when the weather was hot. The water at the sites were analyzed using an on-site water quality meter (YSI ProDSS multiparameter water quality meter, 626870-2, YSI Incorporated, Yellow Springs, OH, USA) before sampling the water for analysis. The water samples for anion and cation analysis were filtered using a 0.45 μm filter, and the cation samples were adjusted to pH 2 or less using strong HCl. The samples were sealed in containers and stored in refrigeration at 4 °C or lower until they were analyzed. The major and minor ion analyses were conducted at the Basic Science Analysis Support Center of the Sangji University. An isotope mass spectrometer (IRMS) was used to analyze oxygen stable isotopes, and a Cavitation Ring-down spectrometer (CRDS) was used to analyze hydrogen stable isotopes in the Beta Analytic Testing Laboratory (Miami, FL, USA). The equipment used in the analysis followed the standards of ISO/IEC 17025:2017. The ion concentration was reported in mg/L and the stable isotope values are indicated in ‰.

3.2. Microbial Analysis Method

The water samples for microbial analysis were filtered using a 0.25-μm filter and stored in a refrigerator at −80 °C, until the 16S rRNA extraction and pyrosequencing were completed, followed by rRNA analysis. DNA was extracted using a DNeasyPowerSoil Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The extracted DNA was quantified using Quant-IT PicoGreen (Invitrogen, Waltham, MA, USA). The sequencing libraries were prepared according to the Illumina 16S Metagenomic Sequencing Library protocols to amplify the V3 and V4 region. The input gDNA 2 ng was polymerase chain reaction (PCR)-amplified with 5× reaction buffer, 1 mM of dNTP mix, 500 nM each of the universal F/R PCR primer, and Herculase II fusion DNA polymerase (Agilent Technologies, Santa Clara, CA, USA). The cycle condition for the 1st PCR was 3 min at 95 °C for heat activation, and 25 cycles of 30 s at 95 °C, 30 s at 55 °C, and 30 s at 72 °C, followed by a 5 min final extension at 72 °C. The universal primer pair with Illumina adapter overhang sequences used for the first amplifications were as follows: V3-forward (F): 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGG NGGCWGCAG-3′; V4-reverse (R): 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG GACTACHVGGGTATCTAATCC-3′. The 1st PCR product was purified using AMPure beads (Agencourt Bioscience, Beverly, MA, USA). Following purification, the 2 µL of the 1st PCR product was PCR-amplified for final library construction containing the index using Nextera XT Indexed Primer. The cycle condition for the 2nd PCR was same as the 1st PCR, except for 10 cycles. The PCR product was purified using AMPure beads. The final purified product was quantified using quantitative PCR (qPCR), according to the qPCR Quantification Protocol Guide (KAPA Library Quantification kits for IlluminaSequecing platforms) and qualified using TapeStation D1000 ScreenTape (Agilent Technologies, Waldbronn, Germany). The paired-end (2 × 300 bp) sequencing was performed by Macrogen using the MiSeq™ platform (Illumina, San Diego, CA, USA). The obtained sequencing reads were deposited to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA; http://www.ncbi.nlm.nih.gov/) under accession number SRP347337.

3.3. Correlation Analysis Method

The similarity pattern in the structure of the microbial community was examined by non-metric multidimensional scaling (NMDS) analysis. The NMDS algorithm ranks the distance between objects and uses this ranking to map objects nonlinearly in a simplified two-dimensional array space to maintain the ranking difference, rather than the distance [26]. In addition, canonical-correlation analysis (CCA) was performed to examine the correlation between the microbial community and the hydrochemical properties of the water samples. Notably, CCA analysis is a method of correlating the linear relationship between two multidimensional variables. It is especially suitable for species abundance analysis and environmental interpretation; most importantly, it accommodates the absence of species at specific locations in the data set [27]. The NMDS and CCA analyses were performed using the “vegan” package of the R for the Windows version 3.2.0 (The R Project for Statistical Computing, Vienna, Austria). This analysis can determine the relationship between the microbial community composition and the hydrochemical properties of water in an area.

4. Results and Discussion

4.1. Hydrochemical Properties

The parameters of the water quality of the A. aquatica habitat (wetland), surface water, and groundwater are provided in Table 2. The average water temperature of the A. aquatica habitat (wetland) was 25.4–29.0 °C, while that of the surface water was similar at approximately 25.1 °C. The groundwater had a low temperature of 16.3–17.4 °C. The A. aquatica habitat and surface water portrayed large seasonal variations, as they were directly affected by the atmosphere, but the groundwater portrayed a relatively small seasonal impact [28]. The pH values of the A. aquatica habitat (wetland) water samples were 7.7–8.4, the surface water samples were 8.1–8.3, and the groundwater samples were 6.7–9.7. The pH of the EDG4 water sample, the site that controlled the water level of the wetland, was 9.7; this appeared to influence the pH value of the wetland. Thus, considering that the pH of the A. aquatica habitat in Japan is 4.5–4.48 [7], the pH of the A. aquatica habitat in South Korea is relatively high. Furthermore, the dissolved oxygen (DO) was 8.7–9.8 mg/L in the wetland, 8.5–9.1 mg/L in the surface water, and 2.8–7.0 mg/L in the groundwater. The electric conductivity (EC) was 50.6–88.9 μS/cm in the wetland, 247.7–328.6 μS/cm in the surface water, and 240.3–540 μS/cm in the groundwater. The oxidation-reduction potential was 47.7–72.3 mV in the wetland, 59.4–73.6 mV in the surface water, and 0.6–98.9 mV in the groundwater. The turbidity was 19.0–37.9 NTU in the wetland, 8.3–11.7 NTU in the surface water, and 0.5–14.4 NTU in the groundwater. Notably, the wetland and surface water samples indicated similar properties. A previous study reported that the A. aquatica habitat in Japan had low pH and DO values [7,8]. This suggests that A. aquatica can prosper in an environment where the natural enemies of A. aquatica cannot survive. Fish, which are common predators of A. aquatica, are unable to survive in water with less than 3 mg/L dissolved oxygen [29]. A. aquatica, on the other hand, does not absorb oxygen from the water, hence they can survive in water with low DO levels. As low DO makes it difficult for possible predators such as fish to live, A. aquatica can survive and prosper in these conditions [7,30].
Therefore, the pH and DO levels in the research area are high. The A. aquatica habitat in Eundae-ri has an environment where the natural enemies of A. aquatica can prosper and this appears to contribute to the reduction in the species population.
The hydrochemical composition of the wetland can help us to better understand the evolution of wetlands and their interactions with different water sources. The Piper diagram is an important tool that reflects the source and type of natural water [31]. The Piper diagram of groundwater, wetland, and surface water collected from the three samplings of the study area is shown in Figure 4. The groundwater well EDG4, which directly controlled the wetland water level, displayed the properties of Na-HCO3 type, and the EDG 3 well portrayed the properties of Ca-HCO3 type. The other groundwater sites, EDG1 and EDG2, and the surface water sites, EDS1 and EDS2, also displayed the properties of Ca-HCO3 type. Moreover, the wetland also displayed Ca-HCO3-type properties, commonly observed in groundwater and surface water.
Most of the Ca-HCO3 types showed the properties of surface water, wetland, and shallow groundwater, which are directly affected by the atmosphere [32,33]. The Na-HCO3 type showed the properties of deep groundwater samples [32]. The water evolved from the Na-HCO3 to Ca-HCO3 type, based on the composition trend of the recharge water that recovered the aquifers [34]. The EDG4 water samples showed the properties of deep groundwater, whereas EDG3, EDG2, and EDG1 showed the properties of shallow groundwater.
In terms of the chemical composition of the water samples acquired from the study area, the concentration of NO3 (Table 3) could not be detected in the wetland samples. The surface water had a NO3 content of 6.99–17.74 mg/L, and the wells EDG1 and EDG2, which were located far from the wetland, had a NO3 content of 7.3–12.9 mg/L. Furthermore, EDG3 and EDG4, which were located close to the wetland, had a content of 0–1.19 mg/L. This indicated that the area around the wetland was clean and not affected by pesticides or chemical fertilizers.
The correlation of hydrogen and oxygen isotopes in the water can be defined as follows. The local meteoric water line (LMWL) can be used to represent the moisture sources and local climatic conditions. Notably, the hydrogen-oxygen scattering deviation of different waters in the LMWL can reflect the degree of evaporation and water interaction [35,36].
The relationship between the δ18O and δ2H values of the surface water, wetland, and groundwater samples analyzed in our study during May–July 2021 and the precipitation in October 2021 are portrayed in Figure 5. As shown in the figure, the isotopes of the surface water and groundwater appear on the same line as the global meteoric water line (GMWL), but the isotopes of the A. aquatica habitat (wetland) portrayed variations in the values due to the climatic impact [37]. In addition, the slope of the wetland drift curve varies, although it always shows a smaller evaporation curve than GMWL [38]. The general slope at this point varies based on the temperature and relative humidity of the region. In contrast to the groundwater and surface water samples, the values of hydrogen isotopes in the wetland water samples were −27.13–−41.76‰, and the values of oxygen isotopes were −2.13–−5.88‰. In the surface water and groundwater samples, the values of hydrogen isotopes were −49.78–−57.13‰, and the values of oxygen isotopes were −7.11–−8.48‰. Notably, the precipitation in October appeared on the GMWL.

4.2. Phylum-Level Analysis

Figure 6 portrays the variations in the composition between the samples at the phylum level. A total of 12 phyla were found in 28 samples. Figure 5 depicts the composition of the bacteria group detected in the samples. The dominant bacteria were Proteobacteria, with their proportions being 80.18–14.90%, followed by Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Nitrospirae. Actinobacteria were present in the range of 31.27–0.18%.
Previous studies have indicated that the length of the branch in the weighted UniFrac Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering can be weighted according to the relative richness of the community sequence [39]. The result of EDG1 May, EDG1 June, EDS2 June, EDG1 July, EDG2 July, EDG4 June, EDG4 July, EDG3 June, EDG3 May, and EDG3 July were combined as Group A, whereas those of EDS1 June, EDS1 July, EDS2 July, EDW3 June, EDW1 May, EDW2 May, EDW3 May, EDS1 May, EDS2 May, EDG2 May, EDW2 July, EDW1 July, EDW1 June, and EDW2 June were combined as Group B.
Group A consisted of microbial communities found in the samples collected from the groundwater wells. Group B consisted of microbial communities found in the samples collected from the surface water and the wetland. EDG 2, a shallow groundwater well used for domestic water needs, appeared in both groups A and B and was relatively affected by the surrounding environment or surface water. A small amount of precipitation (4.5 mm) occurred at the time of sampling of the EDG 2 site (May) in Group B. It was included in the same group as the samples of the surface water and wetland, due to the influence of a large amount of precipitation in May.
Group A was characterized by 37.45–80.18% of Proteobacteria, and Nitrospirae were present only in the EDG1 and EDG4 samples of the group.
Generally, groundwater ecosystems do not show indications of photosynthesis and lack organic carbon. Therefore, the microbial communities in the aquifer were assumed to be mainly composed of heterotrophs that have insufficient nutrients and are well-adapted to the oligotrophic groundwater environment [40,41]. Moreover, autotrophs that fix carbon dioxide and produce energy by oxidizing inorganic electron donors are another important component of the groundwater microbial communities; notably, autotrophs are especially important in deeper underground habitats [42,43,44,45].
Generally, proteobacteria occur in porous alluvial sand and gravel aquifers (5–50 m below surface) [46] and basalt aquifers [47]. According to previous studies, proteobacteria also occur in various freshwater ecosystems, such as lakes [48,49,50] and reservoirs [51,52,53].
Nitrospirae belong to the most diverse nitrite-oxidizing phyla and include several genera, along with the largest genus, Nitrospira [54].
Group B had 5.41–63.27% of Bacteroidetes, along with a high proportion of Actinobacteria (6.10–29.16%). Generally, Bacteroidetes are observed in various environments, including in soil, sea, and animal skin and intestines, and are representative organic decomposers [55]. Actinobacteria are mainly known as soil bacteria, but may be more abundant in freshwater [56]. They play a crucial role in removing complex organic matters, nitrogen, and phosphorous, by decomposing dead organisms in the soil, thus promoting the reabsorption of molecules by plants [57]. Consequently, they can improve the water quality of natural wetlands and restore their bacteria community [58]. Furthermore, in Group B, Verrucomicrobia appeared dominant, except in the wetland samples in July and in the EDG2 sample in May. Notably, Verrucomicrobia are generally distributed in freshwater ecosystems, such as lakes, rivers, and wetlands [59]. Up to 90% of them exist in lakes [60] and generally account for 1–6% of all microbial communities [61,62,63]. Generally, 19% of these organisms are distributed in humic lakes [64].
In our study, the UniFrac UPGMA clustering result indicated that surface water (precipitation) had the largest impact on the wetland.
The dynamics of bacteria communities in the 25 samples were analyzed by Illumina MiSeq sequencing (Table 4). The operational taxonomic units (OTUs) and alpha-diversity indices are summarized in Table 4. The averages of the OTUs were 162 in the wetland, 171 in the surface water, and 166 in the groundwater. The EDW1 sample extracted in June portrayed the highest number of OTUs. When the Chao1 and Shannon indices were averaged on the basis of the water type, they were 181 and 5.65 in the wetland, 192 and 5.69 in the surface water, and 184 and 5.43 in the groundwater, respectively. Thus, the values were the highest for the surface water, but in terms of individual samples, the EDW1 extracted in June portrayed the highest values. This indicated that, compared to wetland and groundwater, the surface water had more microbial diversity. Inverse Simpson and Good’s coverage of library are indices that portray the similarity of diversity of microbial communities in a range of samples; therefore, we used these indices in our study. Notably, the sequencing ranges were similar among the groups.

4.3. Non-Metric Multidimensional Scaling (NMDS) and Canonical Correlation Analysis (CCA)

The microbiology, hydrochemistry, and NMDS correlation analysis of the field data revealed that the DO and Si values portrayed the highest correlation (Table 5 and Figure 7). The DO values were higher in the surface water and wetland samples, compared to those in the groundwater samples. Notably, a strong association was observed between cyanobacteria and DO. Additionally, high contents of the bacteria were also observed in the surface water and wetland samples, compared to the groundwater samples.
Cyanobacteria are autotrophs; in fact, they are the only photosynthetic bacteria that produce oxygen [65]. Previous studies indicate that a positive correlation exists between the total cyanobacteria species and the DO in a contaminated wetland; and generally, a high DO is related to a low cyanobacteria biomass [66]. Therefore, previous studies have deduced that the DO value in water increases in the presence of cyanobacteria, due to their ability to produce oxygen.
Furthermore, the content of Si was less than 6 mg/L in the wetland and surface water samples, but more than 6 mg/L in the groundwater samples. Acidobacteria and Chloroflexi portrayed a similarity; Acidobacteria and Chloroflexi were found in the majority of groundwater samples, but were not found in the wetland and surface water samples. According to the chemical classification of volcanic rocks, basalt, which is a bedrock, contains 45–53 wt% of SiO2 [67], and the Si content was also observed in the groundwater samples. The banded iron formations (BIFs) in Brazil are composed of silica and Fe(III) oxide lamina; notably, previous studies have indicated that certain microorganisms (Chloroflexi, Acidobacteria, and the Alpha-, Beta-, and Gammaproteobacteria) can reduce dissimilatory Fe(III) [68]. Therefore, in our study, Chloroflexi and Acidobacteria that were observed in the groundwater were affected by the basalt bedrock.
CCA was conducted to identify the correlations between microorganisms and environmental parameters (Figure 8). The length of the arrows of the environmental parameters indicated the intensity of the relationship between the parameter and community composition (Figure 8). The result of the CCA analysis portrayed high correlations between the temperature, pH, and DO values of the water samples (Table 6). Temperature portrayed the highest correlation with the wetland water sample, followed by the surface water and groundwater samples. Moreover, a strong correlation was observed between temperature and the population of Cyanobacteria. The highest concentration of Cyanobacteria appeared in the wetland samples that had high temperatures, followed by the surface water and groundwater samples. Most of the cyanobacteria appeared at temperatures of 20 °C or higher, as when they are not under dry conditions, the germination rate increases when the water temperature rises to 20 °C or higher [69].
Notably, the pH showed a high correlation between Verrucomicrobia and Acidobacteria. The surface water and wetland samples had a higher content of Verrucomicrobia, compared to the groundwater samples. Acidobacteria appeared in abundance in the groundwater samples, but were rare in the surface water and wetland samples. In freshwater samples, Verrucomicrobia mainly occur in waters with a low pH [70] or near a site where the groundwater flow has continuously high pH [71]. Therefore, Verrucomicrobia were found in abundance in the EDG4 water sample, where the pH of the water was the highest.
Additionally, DO portrayed strong correlations with Cyanobacteria and Actinobacteria. The contents of Cyanobacteria and Actinobacteria appeared high at the sites where the water had a high DO content. Most of the Actinobacterial communities in the freshwater are aerobic [72]. Furthermore, DO is an important aquatic environmental factor that influences the growth and metabolism of Actinobacteria; notably, DO concentration influences the production of an odor substance by Actinobacteria [73,74]. The environmental factors that influence the A. aquatica wetland in the NMDS and CCA results were DO, temperature, and pH, which were strongly and significantly associated with the variations in the bacterial communities. In our study, the main bacteria related to the environmental impact on the wetland were Cyanobacteria, Actinobacteria, and Verrucomicrobia. Unlike other A. aquatica wetlands, the wetland indicated high DO content and pH values, which also influenced the continuous maintenance of the wetland.

5. Conclusions

To examine the hydroecological environment of the habitat of A. aquatica, field data were analyzed consisting of conventional indices in addition to hydrochemical analysis. In addition, the main factors that influence the microbial analysis and structure of the A. aquatica habitat (wetland), surface water, and groundwater were also investigated. The hydrochemical properties—water temperature, pH, electrical conductivity, dissolved oxygen (DO), oxidation reduction potential, and turbidity—of the surface water and A. aquatica habitat were similar. The Piper diagrams revealed that the wetlands, surface water, and most of the groundwater showed Ca-HCO3-type properties, whereas only areas where the water level of the wetland was controlled displayed Na-HCO3-type properties. The dominant bacteria were Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Nitrospirae. The correlation analysis revealed that the major environmental control factors of the A. aquatica habitat were DO, temperature, and pH, and the related bacteria were Cyanobacteria, Actinobacteria, and Verrucomicrobia. In particular, the chemical and microbiological analysis results showed that the hydrochemical and microbiological properties of the wetland were more affected by the surface water, i.e., precipitation, than groundwater.
According to the existing literature on A. aquatica habitats outside South Korea, the pH and DO values are higher in the habitat in South Korea.
Our findings indicate that the A. aquatica habitat require the management of the main environmental factors, because any limitations in the environmental management of the A. aquatica habitat can be a major factor that can negatively influence the sustainability of the wetland. Moreover, the wetland environment of the A. aquatica habitat is characterized by a high sensitivity to the environmental conditions of the watershed and changes even in the inland environment. Future studies should conduct continuous on-site monitoring and management of major factors in response to climate change.

Author Contributions

Conceptualization, H.K. and J.M.; methodology, H.K. and J.M.; software, H.K. and J.M.; validation, H.K. and H.-S.R.; formal analysis, H.K., J.M. and H.-S.R.; investigation, H.K. and H.-S.R.; resources, H.K. and H.-S.R.; data curation, H.K., J.M. and H.-S.R.; writing—original draft preparation, J.M. and H.K.; writing—review and editing, H.K.; visualization, H.-S.R.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the 2021 Research Project for UNESCO Hantangang River Global Geopark supported by Gyeonggi Provincial Office (Grant No. 20210606641-00) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant numbers 2019R1I1A2A01057002 and 2019R1A6A1A03033167) and 2021 Research Grant from Kangwon National University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. De Bakker, D.; Baetens, K.; Van Nimmen, E.; Gellynck, K.; Mertens, J.; Van Langenhove, L.; Kiekens, P. Description of the structure of different silk threads produced by the water spider Argyroneta aquatica (Clerck, 1757) (Araneae: Cybaeidae). Belg. J. Zool. 2006, 136, 137–143. [Google Scholar]
  2. Namkung, J.; Kim, S.T.; Lim, H.Y. On a water spider, Argyroneta aquatica (Clerck, 1758) from Korea (Araneae: Argyronetidae). Korean Arachnol. 1996, 12, 111–117. (In Korean) [Google Scholar]
  3. Bristowe, W.S. The World of Spiders; Collins: London, UK, 1958. [Google Scholar]
  4. Foelix, R.F. Biology of Spiders; Harvard University Press: Cambridge, UK, 1981; p. 306. [Google Scholar]
  5. Seyyar, O.; Demir, H. Distribution and habitats of the water spider Argyroneta aquatica (Clerck, 1757) (Araneae, Cybaeidae) in Turkey. Arch. Biol. Sci. 2009, 61, 773–776. [Google Scholar] [CrossRef]
  6. Seymour, R.S.; Hetz, S.K. The diving bell and the spider: The physical gill of Argyroneta aquatica. J. Exp. Biol. 2011, 214, 2175–2181. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Masumoto, T.; Masumoto, T.; Yoshida, M.; Nishikawa, Y. Water conditions of the habitat of the water spider Argyroneta aquatica (Araneae; Argyronetidae) in Mizoro pond. Acta Arachnol. 1998, 47, 121–124. [Google Scholar] [CrossRef] [Green Version]
  8. Masumoto, T.; Masumoto, T.; Yoshida, M.; Nishikawa, Y. Time budget of activity in the water spider Argyroneta aquatica (Araneae, Argyronetidae) under rearing conditions. Acta Arachnol. 1998, 47, 125–131. [Google Scholar] [CrossRef] [Green Version]
  9. Mammola, S.; Cavalcante, R.; Isaia, M. Ecological preference of the diving bell spider Argyroneta aquatica in a resurgence of the Po plain (Northern Italy) (Araneae: Cybaeidae). Fragm. Entomol. 2016, 48, 9–16. [Google Scholar] [CrossRef]
  10. Kim, J.P.; Lim, D.H. Ecological studies of Korean Argyroneta aquatica (Clerck, 1757). Korean Arachnol. 2011, 27, 3–48. (In Korean) [Google Scholar]
  11. Brignoli, P.M. Ragni d’Italia XXVII. Nuovi dati su Agelenidae, Argyronetidae, Hahniidae, Oxyopidae e Pisauridae, cavernicoli ed epigei (Araneae). Quad. Mus. Speleol. V. Rivera L’Aquila 1977, 2, 3–118. [Google Scholar]
  12. Dolmen, D. Vannedderkoppen, Argyroneta aquatica Clerck, i Tmndelagsornradet. Fauna 1977, 30, 121–126. [Google Scholar]
  13. Aakra, K.; Dolmen, D. Distribution and ecology of the water spider, Argyroneta aquatica (Clerck) (Araneae, Cybaeidae), in Norway. Nor. J. Entomol. 2003, 50, 11–16. [Google Scholar]
  14. Isaia, M.; Pantini, P.; Beikes, S.; Badino, G. Catalogo ragionato dei ragni (Arachnidia, Aranea) del Piemonte e della Lombardia. In Memorie dell’Associazione Naturalistica Piemontese; Paolo Pantini: Torino, Italy, 2007; Volume IX. [Google Scholar]
  15. Komnenov, M.; Lazarević, P.; Pavićević, D. New records of the water spider Argyroneta aquatica (Clerck, 1757) (Araneae, Cybaeidae) in Serbia. Nat. Montenegrina 2011, 10, 405–414. [Google Scholar]
  16. IUCN (International Union for the Conservation of Nature and Natural Resource). Integrating Biodiversity Conservation and Sustainable Use; Graham Bennett: Gland, Switzerland; Cambridge, UK, 2004; p. 55. [Google Scholar]
  17. Bullock, A.; Acreman, M. The role of wetlands in the hydrological cycle. Hydrol. Earth Syst. Sci. 2003, 7, 358–389. [Google Scholar] [CrossRef] [Green Version]
  18. Shin, Y.H.; Kim, S.H.; Park, S.J. The geochemical roles and properties of mountain wetland in Shinbulsan (Mt.). J. Korean Geomorph. Assoc. 2005, 12, 133–149. (In Korean) [Google Scholar]
  19. Yang, H.K. The hydrological roles and properties of wangdeungjae wetland in Jirisan. J. Korean Geomorph. Assoc. 2008, 15, 77–85. (In Korean) [Google Scholar]
  20. Yang, J.H. Geomorphological characteristics of the water spider habitat in Yeoncheon. J. Korean Geomorph. Assoc. 2018, 25, 77–88. (In Korean) [Google Scholar]
  21. Lim, H.Y. The secret in the pool created by the tank track and habitat discovery of water spider. Nat. Conserv. 1996, 19, 8–11. (In Korean) [Google Scholar]
  22. Kim, J.P. Coloured Spider of Korea; Academybook: Seoul, Korea, 2002; p. 519. [Google Scholar]
  23. Pimm, S.L.; Russell, G.J.; Gittleman, J.L.; Brooks, T.M. The future of biodiversity. Science 1995, 269, 347–350. [Google Scholar]
  24. You, Y.H.; Yi, H.B. Vegetation characteristics, conservation, and ecotourism strategies for water spider (Argyroneta aquatica) in small marsh, Korean Natural Monument. J. Wetl. Res. 2009, 11, 99–106. (In Korean) [Google Scholar]
  25. Lee, M.B.; Lee, S.; Kim, L.; Cha, M. A Study on the ecological and geomorphological environment of the water spider (Argyroneta aquatica) habitat on the Jeongok Lava Plateau in Yeoncheon, Central Korea. J. Korean Geomorph. Assoc. 2016, 23, 85–99. (In Korean) [Google Scholar]
  26. Shepard, R.N. Metric structures in ordinal data. J. Math. Psychol. 1996, 3, 287–315. [Google Scholar] [CrossRef]
  27. Ramette, A. Multivariate analyses in microbial ecology. Fed. Eur. Microbiol. Soc. 2007, 62, 142–160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Lee, J.Y.; Hahn, J.S. Characterization of ground-water temperature obtained from the Korean national groundwater monitoring stations: Implications for heat pumps. J. Hydrol. 2006, 329, 514–526. [Google Scholar] [CrossRef]
  29. Yamagishi, H.; Yoshida, Y.; Fukuhara, H. Methods for Ecology of Aquatic Organisms—Fishes and Bentoth in Fresh Water; Kyoritsu press: Tokyo, Japan, 1976. (In Japanese) [Google Scholar]
  30. Katsura, K.; Nishikawa, Y. On the habitat environment of the water spider. Atypus 1981, 79, 28–29. (In Japanese) [Google Scholar]
  31. Piper, A.M. A graphic procedure in the geochemical interpretation of water analyses. Trans. Am. Geophys. Union 1944, 25, 914–928. [Google Scholar] [CrossRef]
  32. Chadha, D.K. A proposed new diagram for geochemical classification of natural waters and interpretation of chemical data. Hydrogeol. J. 1999, 7, 431–439. [Google Scholar] [CrossRef]
  33. Guseva, N. The origin of the natural water chemical composition in the permafrost region of the eastern slope of the Polar Urals. Water 2016, 8, 594. [Google Scholar] [CrossRef]
  34. Giménez-Forcada, E. Dynamic of sea water Interface using hydrochemical facies evolution diagram. Groundwater 2010, 48, 212–216. [Google Scholar] [CrossRef]
  35. Marchina, C.; Natali, C.; Fazzini, M.; Fusetti, M.; Tassinari, R.; Bianchini, G. Extremely dry and warm conditions in northern Italy during the year 2015: Effects on the Po river water. Rend. Lincei 2017, 28, 281–290. [Google Scholar] [CrossRef]
  36. Qian, H.; Wu, J.; Li, P. Stable oxygen and hydrogen isotopes as indicators of lake water recharge and evaporation in the lakes of the Yinchuan Plain. Hydrol. Processes 2014, 28, 3554–3562. [Google Scholar] [CrossRef]
  37. SAHRA. Sustainability of Semi-Arid Hydrology and Riparian Areas; Isotopes: Oxygen, AZ, USA, 2005. [Google Scholar]
  38. Pang, Z.; Kong, Y.; Li, J.; Tian, J. An isotopic geoindicator in the hydrological cycle. Procedia Earth Planet. Sci. 2017, 17, 534–537. [Google Scholar] [CrossRef]
  39. Lozupone, C.A.; Hamady, M.; Kelley, S.T.; Knight, R. Quantitative and qualitative diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 2007, 73, 1576–1585. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Ghiorse, W.C.; Wilson, J.T. Microbial ecology of the terrestrial subsurface. Adv. Appl. Microbiol. 1988, 33, 107–172. [Google Scholar]
  41. Madsen, E.L.; Ghiorse, W.C. Groundwater microbiology: Subsurface ecosystem processes. In Aquatic Microbiology—An Ecological Approach; Ford, T.E., Ed.; Blackwell Scientific Publication: Oxford, UK, 1993; pp. 167–213. [Google Scholar]
  42. Gold, T. The deep, hot biosphere. Proc. Natl. Acad. Sci. USA 1992, 89, 6045–6049. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Stevens, T.O.; McKinley, J.P. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science 1995, 270, 450–454. [Google Scholar] [CrossRef]
  44. Kotelnikova, S.; Pedersen, K. Evidence for methanogenic Archaea and homoacetogenic Bacteria in deep granitic rock aquifers. FEMS Microbiol. Rev. 1997, 20, 339–349. [Google Scholar] [CrossRef]
  45. Lithoautotrophy in the subsurface. FEMS Microbiol. Rev. 1997, 20, 327–337. [CrossRef]
  46. Wolters, N.; Schwartz, W. Untersuchungen über Vorkommen und Verhalten von Mikroorganismen in reinen Grundwässern. Arch. Hydrobiol. 1956, 51, 500–541. [Google Scholar]
  47. O’Connell, S.P.; Lehman, R.M.; Snoeyenbos-West, O.; Winston, V.D.; Cummings, D.E.; Watwood, M.E.; Colwell, F.S. Detection of Euryarchaeota and Crenarchaeota in an oxic basalt aquifer. FEMS Microbiol. Rev. 2003, 44, 165–173. [Google Scholar]
  48. Spring, S.; Schulze, R.; Overmann, J.; Schleifer, K.H. Identification and characterization of ecologically significant prokaryotes in the sediment of freshwater lakes: Molecular and cultivation studies. FEMS Microbiol. Rev. 2000, 24, 573–590. [Google Scholar] [CrossRef]
  49. Liu, L.; Peng, Y.; Zheng, X.H.; Xiao, L.; Yang, L.Y. Vertical structure of bacterial and archaeal communities within the sediment of a eutrophic lake as revealed by culture-independent methods. J. Freshw. Ecol. 2010, 25, 565–573. [Google Scholar] [CrossRef]
  50. Song, H.; Li, Z.; Du, B.; Wang, G.; Ding, Y. Bacterial communities in sediments of the shallow Lake Dongping in China. J. Appl. Microbiol. 2012, 112, 79–89. [Google Scholar] [CrossRef] [PubMed]
  51. Qu, J.H.; Yuan, H.L.; Wang, E.T.; Li, C.; Huang, H.Z. Bacterial diversity in sediments of the eutrophic Guanting Reservoir, China, estimated by analyses of 16S rDNA sequence. Biodivers. Conserv. 2008, 17, 1667–1683. [Google Scholar] [CrossRef]
  52. Roske, K.; Sachse, R.; Scheerer, C.; Roske, I. Microbial diversity and composition of the sediment in the drinking water reservoir Saidenbach (Saxonia, Germany). Syst. Appl. Microbiol. 2012, 35, 35–44. [Google Scholar] [CrossRef]
  53. Cheng, W.; Zhang, J.X.; Wang, Z.; Wang, M.; Xie, S.G. Bacterial communities in sediments of a drinking water reservoir. Ann. Microbiol. 2013, 64, 875–878. [Google Scholar] [CrossRef]
  54. Ehrich, S.; Behrens, D.; Ludwig, W.; Bock, E. A new obligately chemolithoautotrophic, nitrite-oxidizing bacterium, nitrospira moscoviensis sp. nov. and its phylogenetic relationship. Arch. Microbiol. 1995, 164, 16–23. [Google Scholar] [CrossRef]
  55. Li, J.; Chen, Q.; Li, Q.; Zhao, C.; Feng, Y. Influence of plants and environmental variables on the diversity of soil microbial communities in the Yellow River Delta Wetland, China. Chemosphere 2021, 274, 129967. [Google Scholar] [CrossRef]
  56. Ghai, R.; Rodriguez-Valera, F.; McMahon, K.D.; Toyama, D.; Rinke, R.; Cristina Souza de Oliveira, T.; Wagner Garcia, J.; Pellon de Miranda, F.; Henrique-Silva, F. Metagenomics of the water column in the pristine upper course of the Amazon river. PLoS ONE 2011, 6, e23785. [Google Scholar] [CrossRef]
  57. Servin, J.A.; Herbold, C.W.; Skophammer, R.G.; Lake, J.A. Evidence excluding the root of the tree of life from the actinobacteria. Mol. Biol. Evol. 2008, 25, 1–4. [Google Scholar] [CrossRef] [Green Version]
  58. Zhu, X.; Wang, L.; Zhang, X.; He, M.; Wang, D.; Ren, Y.; Yao, H.; Ngegla, J.; Pan, H. Effects of different types of anthropogenic disturbances and natural wetlands on water quality and microbial communities in a typical black-odor river. Ecol. Indic. 2022, 136, 108613. [Google Scholar] [CrossRef]
  59. He, S.; Stevens, S.L.R.; Chan, L.K.; Bertilsson, S.; Rio, T.; Tringe, S.; Malmstrom, R.; McMahon, K. Ecophysiology of freshwater Verrucomicrobia inferred from metagenome-assembled genomes. mSphere 2017, 2, e00277-17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Zwart, G.; van Hannen, E.J.; Kamst-van Agterveld, M.P.; Van der Gucht, K.; Lindström, E.S.; Van Wichelen, J.; Lauridsen, T.; Crump, B.C.; Han, S.K.; Declerck, S. Rapid screening for freshwater bacterial groups by using reverse line blot hybridization. Appl. Environ. Microbiol. 2003, 69, 5875–5883. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Newton, R.J.; Jones, S.E.; Eiler, A.; McMahon, K.D.; Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 2011, 75, 14–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Eiler, A.; Bertilsson, S. Composition of freshwater bacterial communities associated with cyanobacterial blooms in four Swedish lakes. Environ. Microbiol. 2004, 6, 1228–1243. [Google Scholar] [CrossRef] [PubMed]
  63. Parveen, B.; Mary, I.; Vellet, A.; Ravet, V.; Debroas, D. Temporal dynamics and phylogenetic diversity of free-living and particle-associated Verrucomicrobia communities in relation to environmental variables in a mesotrophic lake. FEMS Microbiol. Ecol. 2013, 83, 189–201. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Arnds, J.; Knittel, K.; Buck, U.; Winkel, M.; Amann, R. Development of a 16S rRNA-targeted probe set for Verrucomicrobia and its application for fluorescence in situ hybridization in a humic lake. Syst. Appl. Microbiol. 2010, 33, 139–148. [Google Scholar] [CrossRef]
  65. Madigan, M.T.; Martinko, J.M.; Buckley, D.H.; Bender, K.S.; Stahl, D. Brock Biology of Microorganisms, 14th ed.; Pearson: London, UK, 2015. [Google Scholar]
  66. Deep, P.; Bhattacharyya, S.; Nayak, B. Cyanobacteria in wetlands of the industrialized Sambalpur District of India. Aquat. Biosyst. 2013, 9, 14. [Google Scholar] [CrossRef] [Green Version]
  67. Le Maitre, R.W.; Bateman, P.; Dudek, A.; Keller, J.; Lameyre Le Bas, M.J.; Sabine, P.A.; Schmid, R.; Sorensen, H.; Streckeisen, A.; Woolley, A.R.; et al. A Classification of Igneous Rocks and Glossary of Terms; Blackwell: Oxford, UK, 1989. [Google Scholar]
  68. Parker, C.W.; Auler, A.S.; Barton, M.D.; Sasowsky, I.D.; Senko, J.M.; Barton, H.A. Fe(III) reducing microorganisms from iron ore caves demonstrate fermentative Fe(III) reduction and promote cave formation. Geomicrobiol. J. 2017, 35, 311–322. [Google Scholar] [CrossRef]
  69. Baker, P.; Bellifemine, D. Environmental influences on akinete germination of Anabaena circinalis and implications for management of cyanobacterial blooms. Hydrobiologia 2000, 47, 65–73. [Google Scholar] [CrossRef]
  70. De Figueiredo, D.R.; Pereira, M.J.; Moura, A.; Silva, L.; Barrios, S.; Fonseca, F.; Henriques, I.; Correia, A. Bacterial community composition over a dry winter in meso- and eutrophic Portuguese water bodies. FEMS Microbiol. Ecol. 2007, 59, 638–650. [Google Scholar] [CrossRef] [Green Version]
  71. Heinzel, E.; Janneck, E.; Glombitza, F.; Schlömann, M.; Seifert, J. Population dynamics of iron-oxidizing communities in pilot plants for the treatment of acid mine waters. Environ. Sci. Technol. 2009, 43, 6138–6144. [Google Scholar] [CrossRef] [PubMed]
  72. Lipko, I.; Belykh, O. Environmental features of freshwater planktonic actinobacteria. Contemp. Probl. Ecol. 2021, 14, 158–170. [Google Scholar] [CrossRef]
  73. Xue, Y.; Shen, J.; Chen, Z.; Ma, J. Isolation and cultivation of actinomycetes causing T&Os and detection of its metabolized T&O compounds. J. Harbin Inst. Technol. 2008, 40, 563–567. [Google Scholar]
  74. Zhang, H.; Ma, M.; Huang, T.; Miao, Y.; Li, H.; Liu, K.; Yang, W.; Ma, B. Spatial and temporal dynamics of actinobacteria in drinking water reservoirs: Novel insights into abundance, community structure, and co-existence model. Sci. Total Environ. 2022, 814, 152804. [Google Scholar] [CrossRef] [PubMed]
Sustainability 14 04988 g001 550
Figure 1. (a) Air bubble formed and carried by a water spider living underwater; and (b) water spider with bubbles swimming in the wetland water in Yeoncheon-gun, South Korea. (c) Distribution of water spider (Argyroneta aquatica) during 1800–2021 (data from www.gbif.org, accessed on 12 January 2022 and Australian museum); the yellow dots indicate their distribution.
Figure 1. (a) Air bubble formed and carried by a water spider living underwater; and (b) water spider with bubbles swimming in the wetland water in Yeoncheon-gun, South Korea. (c) Distribution of water spider (Argyroneta aquatica) during 1800–2021 (data from www.gbif.org, accessed on 12 January 2022 and Australian museum); the yellow dots indicate their distribution.
Sustainability 14 04988 g001
Sustainability 14 04988 g002 550
Figure 2. (a) Map of the Hantangan United Nations Educational, Scientific, and Cultural Organization (UNESCO) Global Geopark; (b) magnified map of the geopark; and (c) water sampling points in the Argyroneta aquatica habitat in the geo park.
Figure 2. (a) Map of the Hantangan United Nations Educational, Scientific, and Cultural Organization (UNESCO) Global Geopark; (b) magnified map of the geopark; and (c) water sampling points in the Argyroneta aquatica habitat in the geo park.
Sustainability 14 04988 g002
Sustainability 14 04988 g003 550
Figure 3. (a) Annual precipitation and average temperature for 10 years and (b) monthly average temperature and precipitation for 2021 in Yencheon.
Figure 3. (a) Annual precipitation and average temperature for 10 years and (b) monthly average temperature and precipitation for 2021 in Yencheon.
Sustainability 14 04988 g003
Sustainability 14 04988 g004 550
Figure 4. Piper diagram portraying the water type of the wetland, surface water, and groundwater samples analyzed in our study.
Figure 4. Piper diagram portraying the water type of the wetland, surface water, and groundwater samples analyzed in our study.
Sustainability 14 04988 g004
Sustainability 14 04988 g005 550
Figure 5. Relationship between the δ18O and δ2H values in wetland, surface water, and groundwater; local meteoric water line (LMWL), global meteoric water line (GMWL).
Figure 5. Relationship between the δ18O and δ2H values in wetland, surface water, and groundwater; local meteoric water line (LMWL), global meteoric water line (GMWL).
Sustainability 14 04988 g005
Sustainability 14 04988 g006 550
Figure 6. Phylum and class level taxonomic classification of bacteria samples and UniFrac Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering of the bacteria communities. Only those phyla having a total dominance of >2% were selected. A: most of the groundwater in the study area, B: most of the surface water and wetland(A. aquatica habitat) in study area.
Figure 6. Phylum and class level taxonomic classification of bacteria samples and UniFrac Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering of the bacteria communities. Only those phyla having a total dominance of >2% were selected. A: most of the groundwater in the study area, B: most of the surface water and wetland(A. aquatica habitat) in study area.
Sustainability 14 04988 g006
Sustainability 14 04988 g007 550
Figure 7. Graph portraying non-metric multidimensional scaling (NMDS) analysis (red labels) for major bacterial species (comprising >2% of the total community composition of each sample) at phylum-level resolution. The blue line and labels correspond to the environmental conditions and geochemical concentrations, and the black labels represent the individual samples. Arrows indicate the correlation vectors of community differences and the process parameters, with significance factors p < 0.001; dissolved oxygen (DO), nephelometric turbidity units (NTUs), total dissolved solids (TDS).
Figure 7. Graph portraying non-metric multidimensional scaling (NMDS) analysis (red labels) for major bacterial species (comprising >2% of the total community composition of each sample) at phylum-level resolution. The blue line and labels correspond to the environmental conditions and geochemical concentrations, and the black labels represent the individual samples. Arrows indicate the correlation vectors of community differences and the process parameters, with significance factors p < 0.001; dissolved oxygen (DO), nephelometric turbidity units (NTUs), total dissolved solids (TDS).
Sustainability 14 04988 g007
Sustainability 14 04988 g008 550
Figure 8. Canonical correspondence analysis (CCA) (red labels) for major bacterial species at phylum-level resolution. The blue lines and labels correspond to the environmental conditions and geochemical concentrations, and the black labels represent the individual samples; dissolved oxygen (DO), nephelometric turbidity units (NTUs), total dissolved solids (TDS).
Figure 8. Canonical correspondence analysis (CCA) (red labels) for major bacterial species at phylum-level resolution. The blue lines and labels correspond to the environmental conditions and geochemical concentrations, and the black labels represent the individual samples; dissolved oxygen (DO), nephelometric turbidity units (NTUs), total dissolved solids (TDS).
Sustainability 14 04988 g008
Table
Table 1. Average temperature and precipitation for 10 years in countries with Argyroneta aquatic habitats.
Table 1. Average temperature and precipitation for 10 years in countries with Argyroneta aquatic habitats.
CountryAverage Temperature (°C)Average Precipitation (mm)
United Kingdom9.31466.4
Ireland10.01306.6
Netherland10.8913.5
Germany10.0688.9
Norway7.08.7
Japan16.51658.9
Australia0.9473.0
Table
Table 2. Values of different parameters measured in field for the water samples of wetland, surface water, and groundwater sites analyzed in our study.
Table 2. Values of different parameters measured in field for the water samples of wetland, surface water, and groundwater sites analyzed in our study.
ParameterTemperature
(°C)		pHEC
(μS/cm)		DO
(mg/L)		ORP
(mV)		Turbidity
(NTU)
EDW1Max37.79.193.39.5111.240.2
Min19.27.486.37.421.426.9
Mean29.08.388.98.753.231.4
SD *9.30.83.81.127.67.6
CV **0.30.10.00.10.50.2
EDW2Max36.99.277.49.172.121.4
Min19.97.266.57.724.217.2
Mean28.88.473.68.647.719.0
SD *8.51.16.20.824.02.2
CV **0.30.10.10.10.50.1
EDW3Max30.27.956.611.085.948.2
Min20.67.644.68.758.727.5
Mean25.47.750.69.872.337.9
SD *6.80.28.51.619.214.6
CV **0.30.00.20.20.30.4
EDS1Max30.78.6281.59.970.510.0
Min16.77.7212.88.345.16.6
Mean25.28.3247.79.159.48.3
SD *7.40.534.40.813.01.7
CV **0.30.10.10.10.20.2
EDS2Max31.88.6388.99.590.615.0
Min17.37.7242.07.154.89.4
Mean25.08.1328.68.573.611.7
SD *7.30.576.91.218.02.9
CV **0.30.10.20.10.20.3
EDG1Max19.17.5284.77.5105.716.1
Min14.26.6257.56.689.00.4
Mean16.37.1269.77.098.26.3
SD *2.50.513.80.58.58.6
CV **0.20.10.10.10.11.4
EDG2Max20.16.7550.05.0112.821.9
Min14.16.6525.04.576.89.0
Mean17.06.7540.04.898.914.4
SD *3.00.113.20.319.46.6
CV **0.20.00.00.10.20.5
EDG3Max19.18.1245.63.571.10.7
Min14.48.0235.42.8−3.30.3
Mean16.88.0240.33.127.30.5
SD *2.40.15.10.438.90.2
CV **0.10.00.00.11.40.4
EDG4Max18.29.7284.13.61.41.0
Min16.69.7280.32.1−0.30.5
Mean17.49.7282.22.80.60.8
SD *1.10.02.71.01.20.3
CV **0.10.00.00.42.20.4
SD *: standard deviation, CV **: coefficient of variation, DO: dissolved oxygen, EC: electric conductivity, OPR: oxidation reduction potential.
Table
Table 3. Results of NO3− in the 25 water samples data analyzed in our study.
Table 3. Results of NO3− in the 25 water samples data analyzed in our study.
SampleNO3− (mg/L)SampleNO3− (mg/L)SampleNO3− (mg/L)
EDW1 Maynon-detectionEDW1 Junenon-detectionEDW1 Julynon-detection
EDW2 Maynon-detectionEDW2 Junenon-detectionEDW2 Julynon-detection
EDW3 Maynon-detectionEDW3 Junenon-detection
EDS1 May17.74EDS1 June9.42EDS1 July6.99
EDS2 May15.38EDS2 June10.86EDS2 July10.29
EDG1 May12.09EDG1 June12.03EDG1 July11.87
EDG2 May7.3EDG2 June9.19EDG2 July8.57
EDG3 May0.87EDG3 June1.19EDG3 Julynon-detection
EDG4 June0.72EDG4 July0.63
Table
Table 4. Operational taxonomic units (OTUs) and alpha-diversity indices of the bacterial communities in the surface water, wetland, and groundwater samples analyzed in our study.
Table 4. Operational taxonomic units (OTUs) and alpha-diversity indices of the bacterial communities in the surface water, wetland, and groundwater samples analyzed in our study.
SampleOTUsChao1ShannonInverse SimpsonGood’s Coverage of Library (%)
EDW1 May158181.216.030.9799.05
EDW2 May139155.155.800.9799.23
EDW3 May137147.125.480.9699.16
EDS1 May143158.005.340.9299.09
EDS2 May117119.405.370.9599.67
EDG1 May260278.506.550.9798.61
EDG2 May111118.584.630.8699.49
EDG3 May111111.145.150.9499.89
EDW1 June310384.386.630.9796.90
EDW2 June152159.165.680.9599.38
EDW3 June5862.503.500.8599.67
EDS1 June151153.375.850.9599.63
EDS2 June108110.155.280.9499.70
EDG1 June186207.145.860.9698.87
EDG2 June5455.004.620.9599.89
EDG3 June150161.004.950.9099.2
EDG4 June168190.964.740.8898.8
EDW1 July146154.005.910.9699.38
EDW2 July196207.326.170.9799.01
EDS1 July236283.286.040.9597.74
EDS2 July275330.866.320.9597.49
EDG1 July204205.496.600.9899.6
EDG2 July5454.004.630.94100
EDG3 July229278.525.660.9597.52
EDG4 July308364.846.440.9697.05
The Chao1 index was used to evaluate the population richness. The Shannon index was used to evaluate the diversity within the bacterial population. It accounted for both species abundance and evenness. The Simpson diversity index was calculated as D = 1 − [Σn(n − 1)/N(N − 1)], where n is the number of individuals of each species and N is the total number of individuals of all species. The Simpson diversity index was the probability that two randomly selected individuals in a given habitat may belong to the same species. Good’s coverage of library was calculated as C = 1 − (s/n), where s is the number of unique OTUs and n is the number of individuals in the species. This index provided a relative measure of how well the sample represented the larger environment.
Table
Table 5. Results of non-metric multidimensional scaling (NMDS) analysis for bacteria in the 25 water samples, with physicochemical data.
Table 5. Results of non-metric multidimensional scaling (NMDS) analysis for bacteria in the 25 water samples, with physicochemical data.
VectorsNMDS1NMDS1r2Pr (>r)
Temperature0.756170.654370.25590.046 *
pH−0.200400.979710.14080.187
EC−0.63930−0.768960.21340.063
DO0.891390.453230.66650.001 ***
TDS−0.66705−0.745020.20580.070
NTU0.99896−0.04559 0.27230.030 *
Ca2+−0.35112−0.936330.11720.236
K+ 0.746720.665140.31780.012 *
Mg2+−0.35251−0.935810.32220.008 **
Na+−0.99727 −0.073830.2950 0.023 *
Si−0.85498−0.518660.64550.001 ***
Cl−−0.01334−0.999910.02580.756
NO3−0.873220.487320.02690.747
SO42−0.74050−0.672060.13510.193
HCO3−−0.72161−0.692300.45450.003 **
CO32−−0.27701−0.960870.20950.049 *
OH−−0.99994−0.010620.02820.792
Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 1. Permutation: free. Number of permutations: 999. Abbreviations: DO: dissolved oxygen; EC: electric conductivity; NTUs: nephelometric turbidity units (NTUs); TDS: total dissolved solids.
Table
Table 6. Results of canonical correspondence (CCA) analysis for bacteria in the 25 water samples analyzed in our study, with physicochemical data.
Table 6. Results of canonical correspondence (CCA) analysis for bacteria in the 25 water samples analyzed in our study, with physicochemical data.
VectorsDfChiSquareFPr (>F)
Temperature10.1327.320.001 ***
pH10.1458.020.001 ***
EC10.0754.170.005 **
DO10.0955.280.001 ***
TDS10.0090.490.834
NTU10.0271.480.199
Ca2+10.0221.210.318
K+ 10.0874.830.004 **
Mg2+10.0653.630.004 **
Na+10.0341.870.115
Si10.0261.430.208
Cl−10.0462.530.037 *
NO3−10.0181.020.403
SO42−10.0291.620.168
HCO3−10.0170.960.451
CO32−10.0201.080.399
OH−10.0251.410.243
Residual70.126
DO: dissolved oxygen; EC: electric conductivity; NTUs: nephelometric turbidity units (NTUs); TDS: total dissolved solids; Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 1. Permutation: free; Number of permutations: 999.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Moon, J.; Kim, H.; Ryu, H.-S. Hydroecology of Argyroneta aquatica’s Habitat in Hantangang River Geopark, South Korea. Sustainability 2022, 14, 4988. https://doi.org/10.3390/su14094988

AMA Style

Moon J, Kim H, Ryu H-S. Hydroecology of Argyroneta aquatica’s Habitat in Hantangang River Geopark, South Korea. Sustainability. 2022; 14(9):4988. https://doi.org/10.3390/su14094988

Chicago/Turabian Style

Moon, Jinah, Heejung Kim, and Han-Sun Ryu. 2022. "Hydroecology of Argyroneta aquatica’s Habitat in Hantangang River Geopark, South Korea" Sustainability 14, no. 9: 4988. https://doi.org/10.3390/su14094988

APA Style

Moon, J., Kim, H., & Ryu, H. -S. (2022). Hydroecology of Argyroneta aquatica’s Habitat in Hantangang River Geopark, South Korea. Sustainability, 14(9), 4988. https://doi.org/10.3390/su14094988

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop