1. Introduction
Soil is the most complex matrix on earth [1,2]. Soil productivity and fertility is driven by the activities of plants and microorganisms that are affected by abiotic factors such as temperature, soil parent material, climate, topography, and time. Because microorganisms are major drivers in the biogeochemical cycles on earth, they can affect the soil chemistry and structure. What are some of the important reactions performed by microorganisms in soil? They are methanogenesis, nitrogen fixation, ammonia oxidation, cellulose biodegradation, etc. [3,4,5]. Therefore, some chemical reactions in the carbon and nitrogen cycles are exclusively performed by microorganisms.
The composition and diversity of bacterial communities in soil are the major factors affecting biogeochemical activity such as carbon and nitrogen cycling. What are the drivers of bacterial community composition and diversity in soil? There are several factors affecting diversity such as carbon content, land management, climate, temperature, etc., but pH has been shown to be most important one [6,7,8,9]. When microorganisms must compete for resources or be subjects of predation and parasitism, these actions also contribute to the stability and diversity of bacterial communities in soils.
What are the most common bacterial phyla found to inhabit soils around the world? There is a predominance of bacteria belonging to the phyla Acidobacteriota, Actinomycetota, Bacteroidota, and Pseudomonadota regardless of geographical location, time, and space [6,7,8,9,10]. Soil bacterial communities worldwide have exhibited different biogeographical distribution patterns where the same taxa are highly abundant in soils regardless of geographical location and time [6,7,11,12,13]. The ability of microorganisms to disperse provides a way to be everywhere, increasing gene flow and horizontal gene transfer and resulting in the limitation of diversification of communities across spatial and geographical locations [14,15,16,17]. However, selective environmental pressures and potential dispersal limitations will promote genetic diversification by mutations and genetic drift. Furthermore, microorganisms can be affected by ecological processes which can be deterministic such as environmental filters and biotic interactions. Stochastic factors such as dispersal limitations and ecological drift can also affect community structure in soils [14,15,18]. For instance, distant and isolated habitats are expected to have unique (endemic) communities due to dispersal limitations and environmental selection (filtering) [19,20,21,22].
When deterministic processes due to biotic and abiotic factors influence the establishment of the community, diversity is severely reduced when compared to stochastic processes. For instance, it has been demonstrated that extreme pH conditions, e.g., acidic or alkaline, impose strong selective ecological pressure on bacterial community structure [18]. When pH is close to 7, stochastic influences are more dominant, leading to phylogenetically less-clustered bacterial communities, while extreme pH environments results in a more deterministic and clustered community. Soil samples taken from geographically distant locations in the Artic region have shown acidic soil with high similarity in their bacterial communities, which indicates the strong influence of deterministic factors [20]. Other studies have demonstrated that the variation of bacterial communities in different isolated ecosystems (islands) are influenced by soil parent materials. Samples taken from the same island exhibited a closer bacterial community affected by pH and geographical distance when compared to other islands [21].
The county of Bergen, located in the northern part of New Jersey (NJ), has the largest state population and is considered a highly developed part (suburb) of the New York (NY) city metropolitan area. The county is based upon 70 municipalities [23] and is one of the most important commercial areas in the United States of America (USA). Soil samples from one of the towns, Paramus, showed similar bacterial community composition and diversity as previous studies worldwide [10]. Most bacteria detected were members of Acidobacteriota, Actinomycetota, Bacteroidota, and Pseudomonadota. No other studies have been reported on the soil bacterial communities of the other municipalities in the county of Bergen, nor has any comparison has been done with soil samples from adjacent states in the USA.
The major objectives of this study were to determine the microbiome of soil samples from four suburban towns in the county of Bergen and to compare the results with one sample from an undisturbed temperate forest in the state of NY and two samples from soils located nearby a highway system in the states of Maryland (MD) and Delaware (DE). Is geographical separation affecting the composition and diversity of bacteria in soils? Is there a closer similarity with closer geographical distribution or farther distance leads to higher dissimilarity? Are the soils from NJ sharing a core microbiome compared to soils from NY, MD, and DE?
2. Materials and Methods
2.1. Soil Sampling
Surface soil samples randomly selected were aseptically collected as previously described from different locations at four different suburban towns in the county of Bergen, NJ, USA [10]. These towns were Fair Lawn (FL), Fort Lee (FTL), Lodi (L), and Weehawken (W). Two other samples were taken at different locations, e.g., 100 m away, from interstate highway 95 North (I-95 N). I-95 N is the main north-south highway connecting southern and northern states and major cities of the eastern seaboard of USA. One sample was taken in the state of DE and another one in the state of MD (Figure 1). A seventh sample came from the state of NY, i.e., undisturbed conifer soil, at the Balsam Lake Mountain Wild Forest in the Catskill Mountains, Hardenburgh, NY.
2.2. DNA Extraction and PCR Amplification from Rhizosphere Soils
Microbial DNA was extracted from soil samples using the ZR Soil Microbe DNA MiniPrep Protocol (Zymo Research, Irvine, CA, USA) as previously described [25]. DNA concentration was determined by using the Qubit® dsDNA HS assay as described by Jimenez et al. [26]. To analyze the quality of the extracted DNA, PCR amplification was performed using primers 341f (CCTACGGGNGGCWGCAG) and 785r (GACTACHVGGGTATCTAATCC), which amplified the V3–V4 fragment of the 16S rRNA gene with size of approximately 465 base pair (bp) [27]. Reaction conditions were as follows: 95 °C for 5 min, followed by 25 cycles consisting of denaturation at 95 °C for 40 s, annealing at 55 °C for 2 min, and extension at 72 °C for 1 min. After the 25 cycles were completed, a final extension step at 72 °C for 7 min was added to the reaction.
Ready-To-Go (RTG) PCR beads (GE Healthcare, Buckinghamshire, UK) were used for each PCR reaction volume as previously described [25]. Reaction mixtures were added to a T100TM thermal cycler (Bio-Rad Laboratories, Hercules, CA, USA) or Mastercycler thermal cycler (Eppendorf Scientific, Westbury, NY, USA). After PCR amplification, amplicon detection was analyzed by gel electrophoresis using the FlashGel system (Lonza Inc., Rockland, ME, USA) as described by Jimenez et al. [25]. A FlashGel DNA Marker (Lonza Inc., Rockland, ME, USA) with fragment sizes ranging from 100 bp to 4 kilo bases (kb) was used to determine the presence of the correct DNA fragments.
2.3. Amplicon Analysis of Bacterial 16S rRNA Genes
Samples from DNA-extracted soils were analyzed by next-generation sequencing of the bacterial ribosomal genes. Next-generation sequencing was performed by Azenta Life Sciences (South Plainfield, NJ, USA) using an Illumina MiSeq protocol (Illumina, San Diego, CA, USA) [10]. Bacteria were identified using primers targeting the hypervariable V3 and V4 regions of the 16S rRNA gene. Operational taxonomic units (OTUs) were grouped based on a 97% identity threshold for data statistics and analysis. Bioinformatic analysis was performed as previously described using Qiime (1.9.1) [10]. Venn diagrams were calculated as described by Behnke-Borowczyk et al. [28]. Jaccard similarity index was calculated as described by Real and Vargas [29].
3. Results
3.1. Alpha Diversity of Bacteria in Soils
Alpha diversity is a reflection of the number of species (richness) and evenness (relative abundance) in the soils. Figure 2 demonstrates that the number of OTU recovered from each soil sample were sufficient to predict species abundance. The L sample showed the highest numbers of OTU. The lowest OTU numbers were detected in sample DE. All NJ soil samples exhibited higher OTU numbers when compared to samples from other states (NY, DE, and MD).
When it came to bacterial diversity, the sample from FTL showed the highest bacterial diversity based on the Shannon Index and Simpson values (Table 1). The sample from MD showed the lowest bacterial diversity. Shannon index values ranged from 6.064 to 4.237, while Simpson values ranged from 0.977 to 0.931. The four suburban soil samples from NJ showed higher diversity than those from NY, DE, and MD.
3.2. Bacterial Communities Based upon Bacterial Phyla
A total of 18 prokaryotic phyla were detected in all seven soil samples. The FL sample showed the highest number with 12, while DE showed the lowest with 7. One archaea phylum was detected in the FL sample, i.e., Crenarchaeota. Most soil samples showed Pseudomonadota (4 out of the 7 soil samples) or Actinomycetota (3 out of 7 soil samples) as the most abundant bacterial phyla. The only sample showing a different pattern was NY, with Acidobacteriota (25.72%) as the second most abundant phylum (Figure 3) followed by Actinomycetota (18.68%) and Chloroflexota (4.18%). In the DE sample, Actinomycetota (49.53%) was the most abundant bacteria followed by Pseudomonadota (38.98%), Acidobacteriota (10.90%) all other bacteria were less than 1%. However, bacteria belonging to the Pseudomonadota (42.59%) were the dominant types in the MD sample followed by Actinomycetota (41.04%), Acidobateriota (12.90%), and Chloroflexota (3.39%), with other phyla showing less than 1%.
Who were the dominant bacteria phyla in the four NJ suburban towns? The highest relative abundance values in FL and FTL were Pseudomonadota. They were the top bacterial phylum, 44.37% and 38.98%, respectively, followed by the Actinomycetota (31.58%, 25.33%) and Acidobacteriota (11.24%, 16.44%). The FL sample showed a very high relative abundance of Nitrospirota with 10.73%. No other soil showed levels higher than 0.58%. The other dominant bacterial phyla were Chloroflexota (8.13%, 4.58%), and Bacteroidota (3.19%, 1.66%). The other two soil samples from NJ (L and W) showed Actinomycetota as the dominant phylum with 49.01% and 48.29%, respectively, followed by bacteria belonging to Pseudomonadota (35.2%, 36.95%). Third highest in L and W were Acidobacteriota (7.74%) and Cyanobacteria (5.41%), followed in both samples by Chloroflexota (5.36%, 3.52%) and Bacteroidota (1.34%, 2.76%).
Venn diagram analysis showed that there were eight (47.10%) bacterial phyla shared between all four suburban soil samples from NJ (Figure 4a). The core microbiome was based on the Pseudomonadota, Actinomycetota, Bacteroidota, Acidobacteriota, Chloroflexota, Gemmatimonadota, Verrucomicrobiota, and Nitrospirota phyla. However, when NJ soil samples were compared with those from DE, NY, and MD, the core microbiome showed lower percentages compared to FTL (35.70%) (Figure 4d), W (33.30%) (Figure 4e), L (35.70%) (Figure 4c), and FL (35.70%) (Figure 4b). The core microbiome between all seven soil samples comprised Pseudomonadota, Actinomycetota, Bacteroidota, Acidobacteriota, and Chloroflexota.
Jaccard similarity index analysis showed that the samples were more similar with closer geographical location when values were close to 1. In general, samples from New Jersey soils (L, W, FTL, FL) were more similar when compared to the NY, MD, and DE samples (Figure 5). The DE and MD samples showed a higher similarity Jaccard index than any other samples (0.75). On the other hand, the DE sample showed the lowest similarity with two NJ samples (W and FTL) (0.39). The NY sample showed the highest Jaccard index with FTL (0.67). The NY sample was closer to the NJ soil samples than MD and DE.
3.3. Soil Bacterial Communities Based upon Genera and Order
How many bacterial genera were detected in the soils? Across all seven soil samples, 37 genera were detected. The highest numbers were found in sample W (30) and the lowest in sample DE (7). The highest relative abundance values in all soils were found to be related to unclassified bacterial genera; the average was 64.82% (Figure 6). Which soil sample had the highest values of unclassified genera? Undisturbed forest soil from NY showed 73.64% of the sequences for which we were unable to provide any identification. The W sample showed the lowest relative abundance of unclassified genera with 58.21%.
What was the most identified bacterial genera in all the soil samples? It was Bradyrhizobium which was the number one genus in 5 out of the 7 soils analyzed. Only samples FL and NY showed a different dominant genus with Rhodoplanes (13.89%, 11.53%, respectively). Both genera belong to the phylum Pseudomonadota. The MD sample showed the highest relative abundance of Bradyrhizobium (27.16%) with FL showing the lowest (4.44%). The highest numbers for Rhodoplanes were found in the FL sample (13.89%) and the lowest in W (3.91%).
When analyzing the most frequently detected genera in the top 10, 13 belonged to the Pseudomonadota phylum (Bradyrhizobium, Rhodoplanes, Burkholderia, Wolbachia, Pedomicrobium, Janthinobacterium, Afifella, Hyphomicrobium, Devosia, Sphingomonas, Kaistobacter, Balneimonas, Propionivibrio), five to Actinomycetota (Actinocordia, Nocardioides, Streptomyces, Mycobacterium, Physicoccus), one to Bacteroidota (Flavobacterium), one to Nitrospirota (Nitrospira), one to Thermodesulfobacteriota (Geobacter), and one to Cyanobacteria (Phormidium).
A core microbiome in NJ soils was detected at the genus level with nine genera (25%) found to be distributed in all soils (Figure 7a). What were the genera comprising the core microbiome? They were Rhodoplanes, Candidatus, Streptomyces, Mycobacterium, Pedomicrobium, Hypomicrobium, Bradyrhizobium and Janthinobacterium. All samples shared the unclassified category. When all the NJ samples were compared to soil from NY, DE, and MD, the core microbiome was smaller in FTL (12.90%) (Figure 7d), W (15.60%) (Figure 7e), L (17.20%) (Figure 7c), and FL (21.10%) (Figure 7b). The core microbiome of the seven soil samples comprised Bradyrhizobium, Rhodoplanes, and Candidatus.
Similarity analysis for total genera showed that the NJ soils were more similar between themselves than when compared to soils from NY, DE, or MD (Figure 8). The MD and DE soils showed closer similarity values (0.88) than any other soils. The NY sample showed the highest similarity with the DE sample (0.42).
There were very high numbers of unclassified bacterial genera. However, higher levels of taxonomical identification provided a better detection to ascertain the community diversity in soils. When sequences were analyzed at the order level, the number of unclassified bacteria were significantly reduced to an average of 4.69% (Figure 9).
A total of 59 bacterial orders were detected in all seven soil samples, with the Rhizobiales (4) and the Actinomycetales (3) showing the highest average percentage of relative abundance (22.08% and 16.50%, respectively). The DE sample showed the highest number of Actinomycetales (38.93%) with sample MD for Rhizobiales (37.13%). The lowest relative abundance for both orders were found in FL (3.62%) and L (16.23%), respectively.
When analyzing the most frequently detected orders in the top 10, six belonged to the Pseudomonadota phylum (Rhizobiales, Xanthomonadales, Burkholderiales, Sphingomonadales, Rhodospirillalles, IS-44), six to Actinomycetota (Gaiellales, Actinomycetales, Acidimicrobiales, Solirubrobacterales, Solibacterales, 0319-7L14), four to Acidobacteriota (Acidobacteriales, Ellin6513, RB41, CCU21), one to Bacteroidota (Saprospirales), one to Nitrospirota (Nitrospirales), one to Chloroflexota (Thermogemmatisporales), one to Thermodesulfobacteriota (Desulfuromonadales), and one to Cyanobacteria (Oscillatoriales).
Venn diagram analysis of total orders per sample was performed to determine the core microbiome between soils. The core microbiome in the NJ soils consisted of 17 bacterial orders (29.8%) (Figure 10a). Only the FL sample from NJ showed a higher percentage of bacterial orders with NY, MD, and DE (30.6%) (Figure 10b). All other NJ soil samples showed lower values in the core microbiome when compared to NY, MD, and DE FTL (27.30%) (Figure 10d), W (26.10%) (Figure 10e), L (26.10%) (Figure 10c). The core microbiome of the seven soil samples comprised Rhizobiales, Xanthomonadales, Burkholderiales, Rhodospirillalles, Gaiellales, Solirubrobacterales, and Saprospirales.
Similarity analysis for total orders showed that the NJ soils were more similar between themselves than when compared to soils from NY, DE, or MD (Figure 11). Only the FL soil exhibited higher similarity values with MD and DE. Again, MD and DE showed the highest similarity index (1.00). The NY sample showed the highest similarity with the NJ soils (0.48) and the lowest with MD and DE (0.44).
4. Discussion
To determine if geographical distance will affect the community assemblage and similarity of bacterial communities, 16S rRNA analyses were performed to determine the microbiome of seven surface soils at different geographical locations in the northeastern part of the USA. The soil bacterial communities were predominantly bacteria belonging to the Pseudomonadota, Actinomycetota, and Acidobacteriota phyla not only at the phylum but also order level. However, the relative abundance values were different for each soil location. Why are these bacterial phyla so dominant? They showed very diverse metabolic capabilities that optimize nutrient cycling, soil fertility, and plant growth [1,11,14,15]. Others, such as Actinomycetota, are major sources of antibiotic and natural products. These bacterial phyla are widely distributed in different soils around world [1,6,8,20,21].
The soil sample from NY, i.e., undisturbed conifer forest, showed higher relative abundance values of Acidobacteriota than any other soil. Forest soils are known to be habitats with very high levels of Acidobacteriota because of the oligotrophic conditions present, which select for these types of bacteria [1,30]. They are adapted to live under low concentrations of organic carbon and acidic conditions reported in temperate and subtropical soils [9,13,21]. The high relative abundance of Acidobacteriota in the sample might indicate possible acidic and oligotrophic conditions present during sampling. Acidobacteriota was present in high relative abundance in soil samples from Central Park in Manhattan, NY [31] and Paramus, NJ [10].
In this study, we found a core microbiome in NJ soils comprised bacteria belonging to the phyla Pseudomonadota, Actinomycetota, Bacteroidota, Acidobacteriota, Chloroflexota, Gemmatimonadota, Verrucomicrobiota, and Nitrospirota. When extending the core microbiome analysis to the other three soils from different states, the common phyla were reduced to only Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, and Bacteroidota. Previous studies from soils worldwide demonstrated the wide distribution of these bacteria influenced by stochastic and deterministic processes [1,6,7,8,9,15,20,32]. Based upon the wide distribution and high relative abundance in all soils, all these phyla exhibited strong dispersal capabilities to colonize different soil habitats regardless of geographical distance and locations. However, deterministic processes such as organic carbon concentration, pH, and nitrogen concentration, as well as other environmental filters, can influence the community structure and diversity of soils [6,11,14,18]. For instance, in the FL sample, the relative abundance of the genus Nitrospira and the phylum Nitrospirota was significantly higher than in other soils. Being capable of performing complete nitrification in soils is one of the major characteristics of these bacteria [33]. When nitrification is very high, nitrogen fixation levels are significantly reduced and the abundance of Nitrospirota bacteria significantly increased [34]. The FL sample went through landscaping on a weekly basis with the addition of fertilizers (land management). This treatment might have promoted higher nitrification rates in the soil with an increase of Nitrospirota orders and genera when compared to other soils which were not managed. Fertilization increases the soil nutrient content, which impacts bacterial growth and metabolic activities, but in some cases does not affect community diversity [35]. Another example of a possible environmental selection was detected in sample W. That sample was the only soil detected with the presence of the phylum Cyanobacteria. Cyanobacteria orders and genera were found only in soil W, indicating the possibility of a high of moisture content during sampling [6]. The presence of Cyanobacteria was previously reported in low relative abundance in French soils [6]. Cyanobacteria are major drivers in the carbon cycle, mostly in aquatic environments, through the fixation of carbon dioxide to carbohydrates during photosynthesis. However, their presence in soils is not rare, due to the soil humidity and sunlight in surface soils [6,10].
The lowest bacterial diversity was detected in soils DE and MD. These soils were sample from locations nearby a highway system. Soils adjacent to highways are exposed to hazardous heavy metals from vehicle emissions and other transportation-generated wastes [36]. These chemicals can be washed by rain or carry by wind penetrating and accumulating into the highway side soil. No environmental analysis of DE and MD soils was performed but the proximity to the interstate highway might have led to the presence of high concentration of pollutants affecting negatively the diversity and composition of the bacterial community. If that was the case a strong environmental filter was introduced to influence the community assemblage and sustainability of bacteria in those soil samples. Urban areas with oligotrophic soil samples, taken from city parks and street medians, did not show a significant decreased in bacterial diversity with Pseudomonadota and Acidobacteriota as the dominant bacterial phyla [37]. In that study, the bacterial composition was different between different types of samples, suggesting a very effective dispersal and enough environmental heterogeneity not to affect bacterial diversity.
In this study, there were two genera with the highest relative abundance in all soils, regardless of geographical location and distance. These genera were Bradyrhizobium and Rhodoplanes. Both genera belong to the phylum Pseudomonadota. Evidently, both bacteria exhibited optimal dispersal rates and successful colonization of different soil environments. Previous studies have shown the predominance of Bradyrhizobium in soils worldwide [8,20,21,38,39]. These bacteria are a dominant member of soil bacterial communities widely distributed in North American soils [38,39]. Bradyrhizobium is a symbiotic nitrogen fixer with legumes. Furthermore, in addition to nitrogen fixation, they can also perform denitrification. During denitrification, nitrogen compounds in the soil are converted to nitrogen gas. Urbanization was reported to influence the abundance of these bacteria by decreasing soil pH [40]. The abundance of Bradyrhizobium was negatively correlated to low pH and ammonia concentration. Nitrogenase activity was significantly inhibited when ammonia concentration increased by the addition of fertilizers to the soils. Bradyrhizobium is highly abundant and widely distributed in North American forest soils. Most of the isolates did not show the presence nodulation (nod) or nitrogen fixation (nif) genes in their genome [38]. Therefore, they were incapable of symbiotic association with plants and nitrogen fixation. However, there was a greater potential to degrade aromatic carbon sources indicated by the presence of a larger set of aromatic degradation pathways, nitrogen metabolism, and polymer degradation genes when compared to symbiotics strains with nitrogen fixation capabilities. Some Bradyrhizobium types are unable to nodulate (non-symbiotic) and establish a symbiotic relationship with plants, but able to fix nitrogen. Evidently, the extremely diverse metabolic capabilities of these bacteria promote the wide distribution across spaces and time.
Rhodoplanes, which were previously reported to be important members in soils worldwide, were also widely distributed in all seven soil samples with high relative abundance values [8,41,42,43,44]. Rhodoplanes belong to the order Rhizobiales within the Pseudomonadota phylum. Their metabolic capabilities ranged from non-sulfur phototrophic growth to heterotrophic growth either aerobically or anaerobically. They do not only contribute to the cycling of carbon but also their contribution to nitrogen fixation in soils cannot be underestimated [43]. These versatile metabolic capabilities might have promoted the wide dispersion of these bacteria regardless of geographical location and time. Unfortunately, unclassified sequences were found to be extremely predominant at the genus level in all soils. These results further support that current databases do not contain enough sequences to identify bacterial communities in bulk soil, since most bacteria are either unculturable or unidentified with no reference sequences available at the genus or higher taxonomic levels [8,9,10,38,44,45,46,47].
Similarity values and Venn diagram analysis demonstrated that the bacterial communities from closer geographical locations in the state of NJ, i.e., FL, FTL, L, and W, shared a higher level of bacterial phyla, genera, order, and core microbiome. However, when compared with soil samples from NY, DE, and MD, community dissimilarity increased with distance. There were some bacterial phylum, orders, and genera distributed widely regardless of the distance such as Pseudomonadota, Actinomycetota, Bradyrhizobium, Rhodoplanes, Rhizobiales, Gailellales, etc. Evidently, these bacteria exhibited stronger dispersal capabilities to other bacteria detected in soil. Several studies demonstrated the aeolian dispersion and colonization of terrestrial environments with pH as one of the most important factors determining the successful colonization of soil niches [48,49,50,51]. Bacterial communities can be dispersed by aerosolized dust originating from leaf surfaces and soil [48]. This aerosolized bacteria can be transported and deposited thousands of kilometers away [49]. However, successful colonization is depending on the local soil properties having neutral pH, thus decreasing the adaptive pressure on bacteria [50]. Extreme pH values reduce the colonization success of some bacterial types. Extreme pH in soils resulted in a less diverse bacterial community driven by deterministic processes, while bacterial communities present in soil with neutral pH were driven by stochastic processes resulting in highly diverse bacterial communities [18]. However, soil samples taken from geographically distant locations in the Artic region have shown acidic soil with high similarity in their bacterial communities, which indicates the strong influence of deterministic factors [20]. Other studies have demonstrated that soil parent material and geographical distance influence soil properties, leading to changes in bacterial community composition and diversity between soils with both stochastic and deterministic processes [21].
Dispersal limitation related to geographical distance is a stochastic process previously demonstrated to determine bacterial diversity and biogeographical patterns in soils from different places around the world [1,14,15,18,20,21,22]. Differences in dispersal limitation might lead to some bacterial communities subjected to environmental selection such as pH, parent material, and carbon concentration. Dispersal capabilities maybe related to high abundance and colonization of different niches. However, lower abundance bacteria such as Nitrospirota, Cyanobacteria, Wolbachia, Sphingomonas, and Acidimicrobiales, showed lower dispersal rates. The difference in relative abundance might be related to dispersal limitation and deterministic factors. Stochastic processes such as dispersal limitations and ecological drift generate specific niches with heterogenous community assembly, while deterministic factors lead to environmental filters where biotic and abiotic factors such as pH, temperature, soil organic carbon, and soil nitrogen, could affect community composition. Other environmental factors can influence community structure and assembly such as moisture and conductivity, which were not measured in this study. Future studies will determine the edaphic factors in soils to determine their impact on community assembly and structure.
Because of the heterogenous nature of soils and the patchiness of bacterial distribution in soils, DNA extraction and PCR analysis of bacterial communities might have introduced a possible bias, where some bacteria were not detected or identified at different taxonomic levels [52,53]. Current genomic databases used for genetic identification are also unable to identify all bacteria present in the environment. However, ongoing updates and the isolation and characterization of the unculturable majority will lead to a better understanding of the community structure and diversity in natural environments [8,11,52,53]. No edaphic factors, such as pH and soil organic carbon, were analyzed in this study. Therefore, other environmental factors might have affected the community composition and diversity of bacteria in the soil samples analyzed.
5. Conclusions
In conclusion, similarity index and Venn diagram analysis showed that geographical distance seemed to determine the composition and diversity of bacterial communities in the seven soil samples analyzed. The greater the distance, the more differences were found between bacterial phyla, orders, and genera. All communities predominantly comprised high relative abundance of Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, and Bacteroidota. The genera Bradyrhizobium and Rhodoplanes showed the highest relative bacterial abundance in all seven soil samples, thus demonstrating wide distribution regardless of distance or geographical location. Nevertheless, other factors, e.g., edaphic factors, might have contributed to the differences and similarities observed in the bacterial communities.
There was no need for institutional review of the study because there were no human samples analyzed.
No consent was required.
The data will be available upon request to the corresponding author.
The author declares no conflict of interest.
Footnotes
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![View Image - Figure 1. Soil sample locations. Maryland (MD), Delaware (DE), Lodi (L), Weehawken (W), Fair Lawn (FL), Fort Lee (FTL), New York (NY). (Source: [24]).](https://media.proquest.com/media/hms/THUM/2/g7Ojb?_a=ChgyMDI1MDgxODEyMzU0ODQwNToyNjc2NzASBzEzODMzMjMaCk9ORV9TRUFSQ0giDTIxNi43My4yMTYuMTgqBzU0NjU5MzUyCjMxNzEwODk3MzQ6C0lubGluZUltYWdlQgEwUgZPbmxpbmVaA0lMSWIEVEhVTWoKMjAyNS8wMS8wMXIKMjAyNS8xMi8zMXoAggEgUC0xMDAwMDg0LTIwMTM5NS1TVEFGRi1udWxsLW51bGySAQZPbmxpbmXKAWdNb3ppbGxhLzUuMCBBcHBsZVdlYktpdC81MzcuMzYgKEtIVE1MLCBsaWtlIEdlY2tvOyBjb21wYXRpYmxlOyBDbGF1ZGVCb3QvMS4wOyArY2xhdWRlYm90QGFudGhyb3BpYy5jb20p0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCG09TOkVNUy1GdWxsVGV4dC1nZXRGdWxsVGV4dMoCD0FydGljbGV8RmVhdHVyZeICAU7yAgD6AgFZggMDV2ViigMcQ0lEOjIwMjUwODE4MTIzNTQ4NDA1OjM1NTM3Ng%3D%3D&_s=U8oRuWQvRnRXbs4xwlrrqU%2F43uE%3D "View Image - Figure 1. Soil sample locations. Maryland (MD), Delaware (DE), Lodi (L), Weehawken (W), Fair Lawn (FL), Fort Lee (FTL), New York (NY). (Source: [24]).")
Enlarge this image.Figure 1. Soil sample locations. Maryland (MD), Delaware (DE), Lodi (L), Weehawken (W), Fair Lawn (FL), Fort Lee (FTL), New York (NY). (Source: [24]).
Enlarge this image.Figure 4. Venn diagram of common bacterial phyla. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial phyla between sites. Numbers in parentheses represent percentage of common bacterial phyla between sites.
Enlarge this image.Figure 4. Venn diagram of common bacterial phyla. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial phyla between sites. Numbers in parentheses represent percentage of common bacterial phyla between sites.
Enlarge this image.Figure 7. Venn diagram of common bacterial genera. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial genera between sites. Numbers in parentheses represent percentage of common bacterial genera between sites.
Enlarge this image.Figure 7. Venn diagram of common bacterial genera. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial genera between sites. Numbers in parentheses represent percentage of common bacterial genera between sites.
Enlarge this image.Figure 9. Relative abundance (percentage) of predominant bacterial orders in soil samples.
Enlarge this image.Figure 10. Venn diagram of common bacterial orders. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial orders between sites. Numbers in parentheses represent percentage of common bacterial orders between sites.
Enlarge this image.Figure 10. Venn diagram of common bacterial orders. (a) Lodi, Weehawken, Fort Lee, Fair Lawn. (b) Fair Lawn, Maryland, Delaware, New York. (c) Lodi, Maryland, Delaware, New York. (d) Fort Lee, Maryland, Delaware, New York. (e) Weehawken, Maryland, Delaware, New York. Numbers represent common bacterial orders between sites. Numbers in parentheses represent percentage of common bacterial orders between sites.
Diversity index based upon Shannon and Simpson values.
| Name | Shannon | Simpson |
|---|---|---|
| Delaware | 4.404 | 0.931 |
| Maryland | 4.237 | 0.900 |
| Fort Lee | 6.064 | 0.977 |
| Lodi | 5.809 | 0.968 |
| New York | 4.777 | 0.945 |
| Weehawken | 5.858 | 0.972 |
| Fair Lawn | 5.275 | 0.962 |
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