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The Influence Of Human Activities On Campus Outdoor Microbial Communities And Seasonal Dynamics

Feb 3

The ground surfaces of functional walls on a large-scale campus mimic a tiny "semi-open community," with disruptions from people and vehicles, and stresses such as temperature and population density varying. As a result, it fills a unique ecological niche for research into microbial ecology in the context of urbanization. We explored outdoor microbial populations in four campuses in Wuhan, China, for this study. Over the course of six seasons, we collected 284 samples from 55 sampling locations, as well as their meteorological and environmental data. Seasonality was found in the organization of campus outdoor microbial communities, which were impacted by a variety of climate conditions. The non-negligible impact of human activities on microbial populations also had a role in this seasonal pattern. Despite changes in microbial composition in response to a variety of stresses, the overall expected function of school outdoor microbial communities remained consistent across campuses. The predicted functions of the spatial–temporal dynamic patterns on campus outdoor microbial communities have bridged the gap between microbial and macro-level ecosystems and provided hints toward a better understanding of the effects of climatic factors and human activities on campus micro-environments.



Gates, teaching buildings, school service buildings, residential quarters, roadways, and other varying-sized structures make up a large-scale campus where residents are exposed to a complex macro- and micro-environment inside a "semi-open community." Students who live in a "semi-open community" seldom leave campus and spend the majority of their time there. In comparison to other places of employment or amusement, as well as nature or an artificial ecological niche, the campus hosts practically everyday social activities, resulting in interruptions from people and vehicle trades. A semi-open community can be roughly defined as a sociological and biological community with restricted access from the outside, with gates and hotels serving as the interfaces, and which is heavily influenced by environmental stressors such as temperature (Bahram et al., 2018) and population density (Yan et al., 2016). Microbes migrate about campuses using moving people and cars as hosts, following the same pathways as the people and vehicles they temporarily inhabit. This serves as a connection between various areas on campus. And, unlike the indoor microbial communities that have been intensively studied, the outdoor microbial ecology on campus represents a novel niche for microbial community characterization. It also fills a significant gap between huge city boroughs (Bik et al., 2016) and a tiny tenant flat. The activities of residents and cars on large-scale campuses may have a significant impact on the environment. However, how human activities impact the geographical and temporal dynamics of campus outdoor microbial communities remains unknown, and systematic and thorough research of the campus outdoor microbiome are still lacking.

Microbes in soil (Wall, 2004; Bossio et al., 2005; Barberán et al., 2011), ocean (Wang et al., 2012; Fuhrman et al., 2015), and lake (Wang et al., 2012; Fuhrman et al., 2015) play an important part in the biogeochemical cycle as well as the regional nutrient cycle (Li et al., 2015; Han et al., 2016; Ward et al., 2017). Microbial community structure and function change according to urban stress (Concepción et al., 2015; Reese et al., 2016), climate (Barberán et al., 2015), and geomorphology (Barberán et al., 2015). (Wang et al., 2012; de Voogd et al., 2015; Sunagawa et al., 2015). The identification and monitoring of significant microbial diversity in varied biological environments became possible with the introduction of sequencing technologies (Loman and Pallen, 2015). Furthermore, broad profiles of interactions between the microbiome and the environment in which it resides have been frequently described, suggesting that it has the ability to affect human health (Hanski et al., 2012; Fujimura et al., 2014). Meanwhile, a longitudinal study of microbial interactions between humans and interior settings found that human influences may significantly affect the microbial pattern on residents' home surfaces (Kembel et al., 2014; Lax et al., 2014; Ruizcalderon et al., 2016; Savage et al., 2016). Similarly, population density within boroughs may alter microbiological pattern (Bik et al., 2016).

Given the potential impacts of the microbiome on the macro-environment (Falkowski et al., 2008) and human health (Barberán et al., 2015), it is critical to understand their interactions in various niches. As a semi-closed community with gates and hotels as the interface, a large-scale campus represents an important niche between large city boroughs (Reese et al., 2016) and occupant apartments, is a special-ecological The indoor bacterial communities on campus have previously been found to have a building-specific and temporally stable pattern (Ross and Neufeld, 2015); however, the outdoor microbial ecology on campus has received less attention. In order to focus on this unique niche of microbial ecology, Huazhong University of Science and Technology (HUST) in Wuhan, China, was chosen, along with three other campuses [Central China Normal University (CCNU), Huazhong Agriculture University (HZAU), and Wuhan University (WHU)] in Wuhan, China, because they have similar greenery coverage and landscape distribution: artificial or natural lakes, small hills, and the arrangement of typical campus buildings.

The four campuses' outdoor microbial communities' spatial and temporal microbial dynamics, as well as their putative environmental causes, were thoroughly investigated. For this study, which is the first of its kind, we looked at the surface microbial populations on campus, collecting 284 samples from 11 different types of locations throughout the year. And we concentrated mostly on the following issues: I How do campus outdoor microbial communities change with the seasons? To explore the historical dynamics of microbial communities, seasonal and other climatic parameters were combined. (ii) Do human activities have an impact on microbial communities? Samples were taken at several HUST locations: first, the Classroom, Canteen, Dorm, and Library were designated as "CCDL," which accommodates a highly packed flow from the east to the west of the campus on a regular basis. Second, non-routine human mobility on campus was represented by Bus Stations, Sports Fields, Clinics, Gates, and Hotels. Third, Hills and Lakes samples were placed in a different category, with low populations most of the time. The influence of human activities was assessed by comparing the microbial makeup of the three groups. (iii) Are outdoor microbial ecosystems on campus functionally robust? To achieve this goal, cross-campus analyses of community taxonomic and functional compositions (refer to Supplementary Information) were done. These studies have given us a unique perspective on the topography of campus outdoor microbial ecosystems, as well as their interactions with environmental conditions and human activity.



Microbial Communities In The Outdoors On Campus: A General Profile

We statistically created taxonomic structure for each sample, with relative abundance (RA) of each species determined at the family level. After quality control, 31,523,358 high-quality reads in total for 284 samples (110,998 reads per sample on average) were acquired with acceptable depth of 16S rRNA gene sequencing. The total number of operational taxonomic units (OTUs) allocated was 541,981. Within each season, the pattern of microbial RA was homogeneous [PERMANOVA test, R2 = 0.199, Pr (>F) = 0.001]. Proteobacteria, Actinobacteria, and Thermi are the most common phyla in outdoor microbial ecosystems on campus during all four seasons. While the RA of other families showed their dominance in the bacterial community in specific seasons, such as Acetobacteraceae (average RA = 0.093), which was enriched in fall.

The dominating species (top three phylum, Proteobacteria, Actinobacteria, and Bacteroidetes) like those in urban soil at Manhattan (Reese et al., 2016), and the community structures mirror those in urban soil at Manhattan (top three phylum, Proteobacteria, Acidobacteria, and Bacteroidetes) (Reese et al., 2016). However, the microbial community patterns identified on campus differ from those reported in forest soil (top three phyla, Proteobacteria, Acidobacteria, and Actinobacteria) (Parks and Beiko, 2010) and in human gut (gut microbiome, Firmicutes > 80% dominance) (Parks and Beiko, 2010). (Ren et al., 2017). In contrast to those identified in Manhattan's urban soil, campus outdoor microbial communities had a distinct phylum: actinobacteria were present as one of the dominating phyla, which was not present in Manhattan's urban soil.


Campus Outdoor Microbial Communities' Seasonality

Seasonal fluctuations in campus outdoor microbial populations were observed at the genus level. At the genus level, species–species networks were built to infer inter-species co-occurrences within the microbial community. The PERMANOVA test revealed a discernible variation between seasons in the integral pattern of campus outdoor microbial populations [R2 = 0.199, Pr (>F) = 0.001]. Based on taxonomic structures throughout six seasons, the sample similarities estimated by Euclidean distance (refer to Supplementary Information) (Schnorr et al., 2014) performed seasonal change with fitted curve of the sine function (T-test, p = 0.191).

Interestingly, average temperature varied cyclically across different sampling locations, which corresponded to seasonal sample similarities.

We looked at the correlations between sample similarities and other environmental parameters including barometric pressure, humidity, and UV index to have a better understanding of the probable causes of the seasonality of the campus outdoor microbial communities. The barometric pressure in Wuhan, China, showed discernible seasonal cyclicality, which was inversely connected with sample similarity (Pearson correlation coefficient, R2 = 0.666). The average UV index was seasonal and had a positive correlation with sample similarity. However, average humidity in Wuhan did not show any evident cyclical tendency, and the association between humidity and sample similarity over seasons was poor.

Some species have a distinct RA during a specific season, which might be used as a seasonal biomarker. For example, Erwinia (LEfSe, LDA = 3.964, p = 2.07e-19, plant pathogenic species) was found to be enriched in spring, Flavobacterium (LDA = 4.681, p = 3.46e-24, freshwater fish pathogen), and Acinetobacter (LDA = 4.364, p = 7.80e-07, soil mineralization) were found to be enriched in summer, and Chryseobacterium.


Campus Outdoor Microbial Communities Are Influenced By Human Activities

On campus, roads linking CCDL support a high flow of students on a regular basis, while other roads show scarce (roads connecting Hills and Lakes) or non-routine (roads connecting Clinics, Gates, Hotels, Bus Station, and Sports Fields) human activity. The comparison of these three groups may give information on the impact of human activities on outdoor microbial populations on campus.

The compositional structure of campus outdoor microbial communities was impacted by the intensity and diversity of activities. The microbial community composition of CCDL, Hill, and Lake was clearly distinguished using principal coordinate analysis (PCoA) and the PERMANOVA test. This distinction was also revealed by the heatmap's results. Because Lake was closer to CCDL than Hill, the steady shift in microbial taxonomic makeup from CCDL to Hill and Lake might explain the dispersing impact of human activities on campus outdoor microbial communities.


Functional Stability In The Face Of Human Activity

Multiple stresses had little effect on the functional stability of campus outdoor microbial ecosystems. The many types of predictable approaches like PICRUSt (Langille et al., 2013), Tax4Fun (Aßhauer et al., 2015), and FAPROTAX (Louca et al., 2016) may be the sole alternative for microbial functional profiling based on the current collected 16S rRNA gene sequencing data. The findings of PICRUSt are shown here, along with those of Tax4Fun and FAROTAX.

Supplementary Dataset 4 is a table including PICRUSt's anticipated functions. Gates and hotels were seen as the crossing points between the interior and outside of campus, where disruptions from people and automobiles may be found. The relative taxonomic difference between Hotels and Gates against others was shown using principal component analysis (PCA). The two comparisons, on the other hand, demonstrated consistent convergence for the expected function. The functional stability of campus outdoor microbial communities in response to human activities was indicated by the significant similarity in functional composition among various partitions.