Inference and Validation of The Structure of Lotka-Volterra Models

For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

Assessment of Seasonal and Vegetational Variations in the Airborne Bacterial Concentration of the Urban Forest Park: A Case Study in Hunan, China

It is important to investigate bacterial air quality in urban forest parks because tree bacteriostasis practices are being increasingly advocated as measures to improve the air quality and public-health in urban green spaces around the world. The aim of the present study was to quantitatively investigate airborne bacterial concentration levels based on field measurements in every season in five selected forest communities and the uncovered space in an urban forest park, as well as the effects of several factors on the culturability of airborne bacteria. Results suggested that the airborne bacterial levels of all forest communities reached the clean air quality standard with regard to the airborne bacteria content, with the highest bacteria concentrations in the uncovered space (1658 ± 1298 CFU/m3), and the lowest in the mixed community (907 ± 567 CFU/m3). The temporal distribution analysis showed that the most airborne bacteria were concentrated in summer, as well as in the morning and afternoon. The bacteriostatic rates of the mixed community were significantly different with seasonal variation (p < 0.05). Spearman’s correlations revealed that the bacterial concentration was significantly positively correlated with the season, wind speed (WS), temperature (T), ultraviolet light (UV), negative air ion (NAI) and total suspended particles (TSP) (p＜0.05), but significantly negatively correlated with the forest community type (p < 0.05). WS, UV, NAI, TSP and PM2.5 showed significant effect on airborne bacterial concentration in a multiple linear regression model (p < 0.05). Overall, the selection of tree species plays a key role in shaping the forest structure and improving air quality, and the urban forest highlights key priorities for future efforts towards a cleaner, healthier, and more diverse regional forest environment.

Inference and Validation of the Structure of Lotka-Volterra Models

For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

Pure-culture and mixed community biofilm responses to carbon-starvation and UV-C exposure /

Biofilms are known to contribute to disease through inherent protective mechanisms and propagation strategies. These multi-cellular systems also play essential roles in numerous environmental processes. The current study investigated the responses of a mixed community biofilm to carbon-starvation, and measured the effects of UV-C on pure-culture biofilms at different stages of maturity by monitoring metabolic and cell yield responses. Carbon dioxide production and biofilm-derived planktonic cell yield were used at the measurement parameters. The mixed community rapidly responded to induced carbon-starvation under continuous flow conditions by remaining metabolically inactive throughout the 96 and 120 h starvation periods, only to promptly return to a metabolically active state upon the reintroduction of carbon. The effects of UV-C on pure-culture biofilms was negligible, with no log activation being achieved, and metabolic activity remaining static.

Pure-culture and mixed community biofilm responses to carbon-starvation and UV-C exposure /

Biofilms are known to contribute to disease through inherent protective mechanisms and propagation strategies. These multi-cellular systems also play essential roles in numerous environmental processes. The current study investigated the responses of a mixed community biofilm to carbon-starvation, and measured the effects of UV-C on pure-culture biofilms at different stages of maturity by monitoring metabolic and cell yield responses. Carbon dioxide production and biofilm-derived planktonic cell yield were used at the measurement parameters. The mixed community rapidly responded to induced carbon-starvation under continuous flow conditions by remaining metabolically inactive throughout the 96 and 120 h starvation periods, only to promptly return to a metabolically active state upon the reintroduction of carbon. The effects of UV-C on pure-culture biofilms was negligible, with no log activation being achieved, and metabolic activity remaining static.

Examining Plasmid Transfer of pJP4 in a Mixed Community Biofilm Formed in a Microcosm Simulating a Porous Aquifer

In this work, a lab-scale microcosm was designed and used to simulate a porous groundwater aquifer. A minimal media acted as the non-selective pressure for plasmid transfer, while a minimal media with gentamicin acted as the selective pressure. PCR, plate counts and confocal laser scanning microscopy were used to monitor transconjugant and donor persistence in the microcosm. The donor was identified through

Examining Plasmid Transfer of pJP4 in a Mixed Community Biofilm Formed in a Microcosm Simulating a Porous Aquifer

In this work, a lab-scale microcosm was designed and used to simulate a porous groundwater aquifer. A minimal media acted as the non-selective pressure for plasmid transfer, while a minimal media with gentamicin acted as the selective pressure. PCR, plate counts and confocal laser scanning microscopy were used to monitor transconjugant and donor persistence in the microcosm. The donor was identified through

Haplotype-level DNA metabarcoding from freshwater macroinvertebrate community samples

DNA metabarcoding is a robust method for environmental impact assessments of freshwater ecosystems that enables the simultaneous multi-species identification of complex mixed community samples from different origins using extracellular and total genomic DNA. The development and evaluation of DNA metabarcoding protocols for haplotype level resolution require attention, specifically for basic population genetic applications, i.e., analysis to allow genetic diversity estimations and dispersal abilities of the species present in the bulk community samples. Various literature has proposed using DNA metabarcoding for population genetics, and few studies have provided preliminary applications and proof of concepts that always refer to particular taxa. However, further exploration and assessment of the laboratory and bioinformatics strategies are required to unlock the potential of metabarcoding-based population-level ecological assessments. Here, we assessed the ability to infer haplotype information of freshwater macroinvertebrate species from DNA metabarcoding community sequence. Using mock samples with known Sanger-sequenced haplotypes, we also assayed the effects of PCR cycle for the detection and reduction of spurious haplotypes obtained from DNA metabarcoding. We tested our haplotyping strategy on a mock sample containing 20 specimens from four species with known haplotypes based on the 658-bp Folmer region of the mitochondrial cytochrome c oxidase (mtCOI) gene. The read processing and denoising-step resulted in 14 zero-radius operational taxonomic units (ZOTUs) of 421-bp length, with 12 ZOTUs having 100% match with 12 of the mock haplotype sequences. The remaining eight haplotypes that were not detected from the DNA metabarcoding dataset were all the A. decemseta samples (0.01, 0.05, 0.10 ng/μL DNA template concentrations), two E. bulba (0.01 and 0.05 ng/μL), E. latifolium (0.01 ng/μL), and two K. tibialis (0.01 and 0.10 ng/μL). Given that most of the undetected samples had low concentrations, we report the influence of initial DNA template concentration on the amplification from a mock community sample. Our observation is in accordance with previous studies that reported that samples or taxa with low DNA template concentrations have lower detection probability. Accordingly, abundant taxa or samples with high biomass tend to have higher detection probabilities than those rare, smaller or have low biomass from mixed-community samples. The difference in biomass affects haplotypes' detection since most of the large specimens would be retained after read processing. Hence, these factors need to be addressed when metabarcoding-based haplotyping is to be used to infer abundance-based analysis for population genetics applications. The phylogenetic-based analysis (Fig. 1) revealed that the two ZOTUs without taxonomic matches clustered with one of the species from the mock sample. This supports our observation that only the samples with low concentration were unrepresented from the DNA metabarcoding data. Although we still reported false positive detections because two of the 14 ZOTUs failed to have a 100% match with the mock reference sequences, we could at least identify them as A. decemseta sequences based on the phylogenetic approach. Quality passing reads relatively increased with increasing cycle number, and the relative abundance of each ZOTUs was consistent for each cycle number. This suggests that increasing the cycle number, from 24 to 64, did not affect the relative abundance of quality passing filter reads. Our study demonstrated that DNA metabarcoding data could be used to infer intraspecific variability, showing promise for possible applications in population-based genetic studies. As DNA metabarcoding becomes more established and laboratory protocols and bioinformatics pipelines are continuously being developed, our proof of concept study demonstrated that the method could be used to infer intraspecific variability, showing promise for possible applications on population-based genetic studies.