Do composition and diversity of bacterial communities and abiotic conditions of spring water reflect characteristics of groundwater ecosystems exposed to different agricultural activities?

Abstract Modern agricultural practices have undeniably increased global food production. On the other hand, agricultural practices not only lead to a degradation of natural ecosystems but also affect the functioning of ecosystems and the related services they provide. Even though impacts of anthropogenic activities vary across ecosystems, freshwater ecosystems are among those affected to a higher degree. In comparison to surface water ecosystems, groundwater ecosystems are less affected by anthropogenic pollutants, as the overlaying soil retains organic and inorganic substances. However, it has become evident that the excessive use of fertilizers has led to the eutrophication of many aquifers. Bacterial communities, which significantly contribute to the cycling of matter due to their metabolic capacities, are prone to environmental perturbations, and structural variation of bacterial communities may consequently affect the functioning of groundwater ecosystems. Our present paper intends to evaluate the impact of anthropogenic activities on environmental conditions as well as on the structural properties of bacterial communities in groundwater. We repeatedly sampled emerging groundwater at five spring sites belonging to different catchments and determined the concentration of abiotic variables as well as the diversity and composition of bacterial communities on a local scale. We hypothesized that anthropogenic activities influence the concentration of abiotic variables, especially of nitrate, as well as the composition and diversity of bacterial communities in groundwater. Our results show that underground spring catchment areas only slightly differ regarding the concentration of abiotic variables as well as the structure of bacterial communities. Furthermore, abiotic variables, presumably influenced by anthropogenic activities, do not correlate with the diversity and composition of bacterial communities. Although supported only by circumstantial evidence, we suggest that upwelling groundwater from the deeper aquifer affects the diversity and composition of bacterial communities, and we argue that bacterial communities act as useful indicators for environmental changes.

freshwater ecosystems are among those affected to a higher degree. In comparison to surface water ecosystems, groundwater ecosystems are less affected by anthropogenic pollutants, as the overlaying soil retains organic and inorganic substances.
However, it has become evident that the excessive use of fertilizers has led to the eutrophication of many aquifers. Bacterial communities, which significantly contribute to the cycling of matter due to their metabolic capacities, are prone to environmental perturbations, and structural variation of bacterial communities may consequently affect the functioning of groundwater ecosystems. Our present paper intends to evaluate the impact of anthropogenic activities on environmental conditions as well as on the structural properties of bacterial communities in groundwater.
We repeatedly sampled emerging groundwater at five spring sites belonging to different catchments and determined the concentration of abiotic variables as well as the diversity and composition of bacterial communities on a local scale. We hypothesized that anthropogenic activities influence the concentration of abiotic variables, especially of nitrate, as well as the composition and diversity of bacterial communities in groundwater. Our results show that underground spring catchment areas only slightly differ regarding the concentration of abiotic variables as well as the structure of bacterial communities. Furthermore, abiotic variables, presumably influenced by anthropogenic activities, do not correlate with the diversity and composition of bacterial communities. Although supported only by circumstantial evidence, we suggest that upwelling groundwater from the deeper aquifer affects the diversity and composition of bacterial communities, and we argue that bacterial communities act as useful indicators for environmental changes.

| INTRODUC TI ON
Global food production has been successfully increased due to the conversion of natural landscapes for agricultural usage in combination with modern agricultural land use practices (Foley et al., 2005). However, agriculture influences the composition and functioning of ecosystems directly and indirectly, resulting in a degradation of ecosystems and their related services, some of which are also relevant for agriculture (Foley et al., 2005;Tilman et al., 2001).
Eutrophication, caused by the excessive application of fertilizers, is one of the major threats that alters the composition and functioning of ecosystems (Tilman et al., 2001). Even though the impacts of eutrophication vary in severity between ecosystems, freshwater environments are considered to be more greatly affected than terrestrial ecosystems (Dudgeon et al., 2006).
Groundwater, which comprises 96% of the usable freshwater (Shiklomanov & Rodda, 2004), is one of the world's most extracted renewable resources for agricultural, industrial and domestic purposes (Morris et al., 2003). The exploitation of groundwater has expanded since the middle of the 20th century due to advances in geological knowledge, well drilling, pump technology, and rural electrification (Foster & Chilton, 2003). The extraction of groundwater has certain advantages in comparison to surface water; the drought resilience of groundwater allows extraction during dry seasons or during long interannual droughts, and additionally the spatial extension of groundwater enables local extraction (Döll, 2009;Kundzewicz & Döll, 2009;Morris et al., 2003). Most importantly, groundwater aquifers generally exhibit longer water retention times, fostering natural attenuation (Foster & Chilton, 2003;Haag & Kaupenjohann, 2001;Morris et al., 2003;Scow & Hicks, 2005) which is why groundwater is considered to be less vulnerable to anthropogenic pollutants than surface waters (Foster & Chilton, 2003).
However, the vulnerability of groundwater may vary and is generally influenced by aquifer type, permeability, and chemical characteristics (Morris et al., 2003). With regard to these characteristics, aquifers may act as conduits or retention compartments, both affecting natural attenuation (Haag & Kaupenjohann, 2001). Water quality improvement is a complex process involving manifold factors that together drive the attenuation process. For example, nitrate, a major contaminant in groundwater (Böhlke, 2002), can be substantially removed in retention compartments within the aquifer due to denitrification processes (Böhlke, 2002;Haag & Kaupenjohann, 2001;Rivett, Buss, Morgan, Smith, & Bemment, 2008). High concentrations of nitrate in groundwater by comparison can be attributed to the excessive application of fertilizers in areas of intensive agricultural land use (Böhlke, 2002). Since the capacity of the soil to retain nitrogen is often exceeded (Böhlke, 2002;Haag & Kaupenjohann, 2001), nitrate can reach groundwater aquifers in vast amounts, resulting in a degradation of groundwater quality (Peters & Meybeck, 2000).
Apart from alteration of the abiotic environment, eutrophication is likely to influence the taxonomic composition of bacterial communities. These alterations are often induced by interspecific and intraspecific interaction. Since microorganisms constantly compete for resources, an increase in nutritional resources may foster competition among species (Hibbing, Fuqua, Parsek, & Peterson, 2010), resulting in a loss of species that have been outcompeted by others (Kotsyurbenko, Glagolev, Nozhevnikova, & Conrad, 2001;Portal-Celhay & Blaser, 2012). Due to the removal of one or several fractions from the community, opportunistic community members may increase in abundance and thus shape the community composition (Costello, Stagaman, Dethlefsen, Bohannan, & Relman, 2012;Lawrence et al., 2012). Apart from competition, species composition can change because species fail to adapt to the newly established environmental conditions (Lawrence et al., 2012). Although tested under controlled laboratory conditions, Lawrence et al. (2012) showed that co-occurring species were able to modify their environment and thus altered the selection pressure on other species.
The alteration of species composition poses a problem insofar as microorganisms play a dominant role in the natural attenuation of contaminants in groundwater ecosystems (Balke & Griebler, 2003), and it was shown that a reduction in microbial diversity can, for example, affect the cycling of nitrogen in soils (Philippot et al., 2009).
The ability of microorganisms to metabolize and biodegrade pollutants is based on the variety of metabolic functions (functional diversity) performed by microorganisms (Danielopol, Griebler, Gunatilaka, & Notenboom, 2003;Goldscheider, Hunkeler, & Rossi, 2006), which provides and sustains high groundwater quality. Although it has yet to be resolved to what extent taxonomic diversity can alter functional diversity (Griffiths et al., 2000;Petchey & Gaston, 2002), it is assumed that ecosystem functioning in general is affected by changes in taxonomic diversity (Díaz, Symstad, Chapin, Wardle, & Huenneke, 2003;Graham et al., 2016).
The aim of this paper was to compare the composition and diversity of bacterial communities as well as associated abiotic variables of underground spring catchment areas on a local scale. We hypothesized (1) that the concentration of abiotic variables, especially the concentration of nitrate, which we suggest to indicate anthropogenic activities, differs between sampling sites. We furthermore hypothesized that (2) bacterial diversity estimates differ between sampling sites in relation to differences in abiotic variables, and (3) bacterial community composition (BCC) differs between underground spring catchment areas and is shaped by abiotic variables.

K E Y W O R D S
anthropogenic activities, bacterial community composition, bacterial diversity, underground spring catchment area 2 | ME THODS

| Study area
The study area is a hilly region (max. elevation + 187 m·a·s·l) called "Baumberge" (BB) located west of the city of Münster, situated in North Rhine-Westphalia, Germany ( Figure 1). The geology of the study area is characterized by deposits of the upper Cretaceous and can be divided into two layers. The upper layer ("Baumberge layer") consists of sand marl and lime marl stones, and due to its crevasse formation and porosity, is permeable to water. The lower layer ("Coesfeld layer") consists of clay and lime marl stones, and is less permeable to water. In the subsurface, the "Coesfeld layer" exhibits a bowl-like structure. The study area is the highest elevation in the otherwise flat landscape of the Westphalian Lowland and represents a precipitation barrier. Rain water seeps through the fissured "Baumberge layer" and accumulates on top of the lower "Coesfeld layer". The rain water circulates in the porous aquifer and emerges in the form of springs at the edge of the "Coesfeld layer" (Göbel, 2010). The study area can furthermore be considered a hydrographical knot, as springs in the area are tributaries belonging to the adjacent catchment areas of five distinct streams: "Münstersche Aa", "Stever", "Steinfurter Aa", "Berkel", and "Vechte" (Düspohl & Messer, 2010). We sampled five springs of four distinct catchment areas in October 2014, December 2014, January 2015, and March 2015 ( Table 1). The sampling sites exhibit similar geological characteristics, and previous analyses of water samples revealed only slight differences in the concentration of main chemical components. Analyses furthermore showed that the springs are anthropogenically influenced, indicated by varying and elevated nitrate concentrations, suggesting the influence of agricultural land use (Hafouzov, 2010).
F I G U R E 1 Geographic location of sampling sites. Germany is indicated in black in the inset map, and the state of North Rhine-Westphalia in pale gray. The region where the sampling was done is shown as a red rectangle. Sampling sites are indicated by the red dots

| Spring water sampling
In total, spring water samples were collected at five locations (BB1-BB5) at four different points in time. The sample names are based on the location as well as on the time of collection (e.g. BB1.1: Site BB1 at time point 1). Two water samples were collected at each sampling point. The first water sample (1 L) was used for the analyses of microbial community structure, and the second water sample (200 ml) was used for the analyses of abiotic parameters.
Simultaneously to water sample collection, in situ parameters of spring water (temperature (T), electric conductivity (EC), pH-value, and dissolved oxygen concentration (DO)) were measured in a beaker using field measurement equipment (Cond 3310, pH 3310, Oxi 3310, WTW, Germany). Samples were extracted using a hand pump and collected in autoclaved glass bottles. All devices were rinsed three times with 400 ml of spring water prior to sample collection. All water samples were kept on ice and in the dark until further processing in the laboratory on the same day.

| Analysis of abiotic conditions
The concentration of abiotic variables was measured directly after retrieval of samples to the laboratory. The concentration of potassium (K + ), sodium (Na + ), strontium (Sr 2+ ), magnesium (Mg 2+ ), calcium Corporation, Japan), using the procedure via nonpurgeable organic carbon (NPOC). All measurements were conducted following standardized norms. Since the concentrations of fluoride, phosphate, and aluminum were below the limit of detection for the majority of samples, these parameters were excluded for further analyses as they could not be analyzed statistically.

| DNA extraction
DNA was extracted as detailed in Karczewski, Riss, and Meyer (2017

| Abiotic conditions
The results of abiotic conditions reveal that five of the fifteen abiotic variables differ significantly in concentration between the five sampling sites notably for variables strontium, magnesium, nitrate, and sulfate (Table 2).
Differences in variables are especially evident between sampling sites BB3 and BB5 ( Figure 2H   The comparison of calculated diversity estimates (Figure 3) shows that sampling sites differ significantly in the expression of Shannon diversity and Pielou's evenness (Table 4; Figure 3B and D).

| Bacterial diversity
However, sampling sites do not differ significantly regarding OTU F I G U R E 2 (A-P) Environmental variables measured at each of the five sampling sites indicated on the x-axis. Bars show means plus 1 standard deviation with n = 4. Where letters above the mean values for a variable are not the same, it indicates that the means are significantly different from each other. There was no global effect of sampling site on panels containing no letters. For location of the sampling sites, see Table 1 abundance and Simpson diversity index (Table 4). The results show a tendency for sampling site BB3 to harbor the highest Shannon diversity and differ significantly from the other springs with the exception of BB2 ( Figure 3B). Furthermore, sampling site BB3 has the highest value for Pielou's evenness, and differs significantly from all other sampling sites ( Figure 3D).

| Relative taxonomic composition
The majority of sequences obtained from each sampling site is assigned to the Proteobacteria phylum (80%) ( Figure 4A). The proportion of rare phyla, which is phyla with an individual relative abundance of less than 5% (represented by "Other", Figure 4A and B) compose on average 9.5% of the overall abundance. While sampling sites display similar patterns in relative taxonomic composition at the phylum level, a more specific pattern can be seen at the family level ( Figure 4B).

| Bacterial community composition
We identified a significant effect of sampling site on the variation of BCC (p = 0.031; PERMANOVA), although site only accounts for 31% of the variation across springs. Differences in bacterial taxonomic composition between spring BB3 and the other springs are also revealed by analysis of principal coordinates ( Figure 5). While samples of sites BB1, BB2, BB4, and BB5 do not display site-specific BCCs, samples from site BB3 exhibit a similar community composition across sampling times ( Figure 5). The correlation of abiotic variables against the ordination plot revealed that the discriminating factors of BCC for the sampling sites can be mainly explained by strontium and silicate ( Figure 5; Table 5).

F I G U R E 4 (A and B)
Relative bacterial abundance of samples at different taxonomic levels. The legends to the right of the figures show the dominant taxa, i.e. those with a relative individual abundance >5%, and rare taxa, i.e. those with a relative individual abundance <5% ("Other"). Sampling site information is indicated on the x-axis. For location of the sites, see Table 1. The first number indicates the sampling site, the second number refers to time of sampling

| D ISCUSS I ON
In this study, we collected water from five distinct sampling sites with similar geological characteristics in underground spring catchment area. We tested whether measured abiotic variables as well as the diversity and composition of microbial communities differ between sampling sites, and whether microbial communities reflect the differences in abiotic variables.
The concentrations of abiotic variables were generally within the range of groundwater of similar geological composition (Bakalowicz, 1994). Additionally, the concentrations of the majority of measured abiotic variables did not differ across sampling sites, and correspond to findings of previous studies in the region (Hafouzov, 2010).
Since spring water is fed by groundwater from underground spring catchment areas, and since the main chemical characteristics of groundwater are related to aquifer geochemistry (Bakalowicz, 1994;Griebler & Mösslacher, 2003), our results indicate the influence of similar hydrological conditions. However, five abiotic variables differed across sampling sites predominantly for the variables strontium, magnesium, nitrate, and sulfate (Table 2). Differences in the concentration of and variation in nitrate across sampling sites were previously identified, suggesting the influence of agricultural land use (Hafouzov, 2010). Elevated nitrate concentrations are known to result from agricultural land use practices (Bakalowicz, 1994) (Hafouzov, 2010) and shows the highest concentrations at sampling sites BB1 and BB5, differing significantly from the other three sampling sites. The results thus partly support our hypothesis (1) that underground spring catchment area characteristics would only differ in those abiotic variables that are influenced by anthropogenic activities.
Regarding our hypothesis (2) that bacterial diversity differs between sampling sites in relation to differences in abiotic variables, we found that differences in bacterial community diversity were only evident for spring BB3, and only for the parameters Shannon diversity and Pielou's evenness. Site BB3 displayed the highest bacterial diversity as well as the most even composition of bacterial OTUs ( Figure 3B and D  (Braun, Schröder, Knecht, & Szewzyk, 2016;Wright et al., 2017). Due to the fact that geochemical characteristics are the main drivers of BCC (Constancias et al., 2015), and since geochemical characteristics differ only slightly across sampling sites, we suggest that this results in a similar taxonomic community composition at the phylum level. This was not unexpected and confirms observations reported by Staley and Sadowsky (2016)  between sampling sites were more apparent at the family level. In conjunction with the results of bacterial diversity, it is most likely that significant differences in diversity estimates across sampling sites are driven by the proportion of rare families, notably for sampling site BB3, where rare families compose approximately 61% of the overall abundance ( Figure 4B). The influence of rare families on bacterial diversity is furthermore supported by the fact that diversity differs significantly across sampling sites for Shannon diversity, but not for Simpson's diversity. This finding likely results from the fact that Shannon diversity is equally sensitive to rare and abundant species, whereas Simpson's diversity index is more sensitive to abundant species (Morris et al., 2014;Vuono et al., 2015). According to the intermediate disturbance hypothesis (Connell, 1978), disturbance prevents competitive species from dominating a given habitat while enabling space for colonization of less competitive species (Gibbons et al., 2016), which is why an increase in the number of rare species is indicative of disturbance (Piper, Siciliano, Winsley, & Lamb, 2015). Furthermore, circumstantial support for disturbance is the fact that the disturbance-tolerant phylum, Bacteriodetes (Kim, Heo, Kang, & Adams, 2013), compose approximately 14% of bacterial communities sampled at spring BB3 ( Figure 4A), although differences were found to be significant only in comparison to spring BB4 (p = 0.03; Wilcoxon rank sum test).
Differences between sampling sites regarding bacterial diversity and relative taxonomic abundance are furthermore confirmed by analysis of principal coordinates of bacterial OTU composition at the family level. We found a significant effect of sampling site on OTU composition (R 2 = 0.311; p = 0.031; PERMANOVA), a result that was previously reported by others (Kaiser et al., 2016;Ma, Ibekwe, Yang, & Crowley, 2016;Tardy et al., 2015). However, the effect of sampling site on OTU composition was low (31%) in comparison to findings reported by Ma et al. (2016) (50.9%), and is only apparent for samples collected at sampling site BB3 ( Figure 5). Hence, variation in community composition must be partly accounted for by variables other than sampling site.
The correlation of BCC with measured variables revealed that strontium and silicate are discriminating drivers of community composition across underground spring catchment areas (Table 5; Figure 5). The results therefore support our hypothesis (3) that BCC differs between sampling sites and is shaped by abiotic variables. However, the results were unexpected, as only strontium was identified to differ between underground spring catchment areas, compared to silicate. Furthermore, formerly reported differences in phosphate concentrations (Hafouzov, 2010) cannot be supported by our data. Interestingly, nitrate, which we hypothesized would influence bacterial communities, is negatively correlated with strontium but had no significant effect on either the composition or diversity of bacterial communities, which is in contrast to results reported from other studies (Ben Maamar et al., 2015;Carrino-Kyker, Smemo, & Burke, 2012;Ibekwe, Ma, & Murinda, 2016;Turlapati et al., 2013;Zhang et al., 2016). Nitrate might thus either not be a discriminating factor for the variation in BCC, or the effect of strontium overrides the effect of nitrate.
A possible explanation for the correlation between strontium and BCC may derive from the fact that the deeper groundwater in the study area exhibits high concentrations of strontium (Hafouzov, 2010). It was shown by previously conducted studies that variations in strontium concentration in spring water were used to indicate the influence of deep groundwater (Bakari et al., 2013;Barbieri, Nigro, for bacterial species, we hypothesize that strontium could also be considered a surrogate for other environmental variables not analyzed in the present paper. Notably, strontium and nitrate are negatively correlated, and it is possible, that the effects of strontium override the effects of nitrate regarding influence on BCC.
However, the presented results indicate that the analysis of abiotic environmental variables in combination with the analysis of BCC has the potential to be used to assess environmental conditions as well as to reflect and trace influences that shape the environment. The data we present provide a limited snapshot of the Baumberge springwater bacterial communities; future studies with repeated samples within sampling time, will be necessary to uncover the full extent to which the composition and diversity of these communities vary, both temporally and spatially.

ACK N OWLED G M ENTS
We would like to thank Nils Weckwert and Marius Römer for assistance during the field work, and Hildegard Schwitte, Barbara Hasert, Robert Peuß, and Alexandra Reschka for technical assistance. We furthermore like to thank Sophie Armitage for comments on manuscript drafts. Additionally, we thank Christoph Schäfers of the Frauenhofer Institute for Molecular Biology and Applied Ecology IME for his support.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest.