Biology subjects

Emmett, B.

Publications and source records attributed to Emmett, B..

4 recordsLinked to original sources

bioRxiv2026

Environmental filtering shapes divergent bacterial strategies and genomic traits across soil niches

Soil pH is a predominant factor in structuring microbial communities; however, its role in shaping microbial life-history traits across large spatial scales remains underexplored. Here, we hypothesised that bacterial ubiquity, or niche breadth, across a diverse collection of soils is linked to genomic traits. We leveraged a national-scale survey of UK soils (the Countryside Survey) and 16S rRNA gene sequencing data with trait annotations (estimated genome size, coding density, and rRNA operon copy number) to examine trait-environment-niche breadth relationships. Our analyses revealed that soil pH was the dominant environmental driver of niche classification and bacterial community traits along the niche range. Low pH soils (pH <5.5) hosted ubiquitous taxa with larger genome sizes, lower coding densities and lower rRNA copy numbers, implying slower growing taxa with higher genetic facilities. Mildly acidic soils (pH 5.5 to 7) favour higher rRNA copy numbers, intermediate genome sizes and moderate coding densities. Alkaline soils (pH >7) feature communities with the smallest niche range, smallest genomes and highest coding densities. Here, specialisation occurs through streamlining with simpler, smaller genomes favoured. We found that generalist taxa were widespread across the pH range, becoming dominant under acidic conditions, while taxa adapted to higher pH were comparatively scarce in their distribution. These findings identify soil pH as a key physiological filter that aligns microbial genomic traits and ecological strategies across landscapes. By extending prior site-specific results to a broad-scale context, our study highlights how trait-based metrics can predict microbial responses to soil conditions, with implications for understanding ecosystem carbon cycling and informing land management practices aimed at sustaining soil health in the future.

microbiology↗

bioRxiv2023

A diversity of diversities: evaluating the relationships between below and aboveground biodiversity

O_LITo predict how biodiversity will respond to global change, it is crucial to understand the relative roles of abiotic drivers and biotic interactions in driving associations between the biodiversity of disparate taxa. It is particularly challenging to understand diversity-diversity links across domains and habitats, because data are rarely available for multiple above- and below-ground taxa across multiple sites. C_LIO_LIHere we analyse data from a unique biodiversity dataset gathered across a variety of oceanic temperate terrestrial habitats in Wales, comprising 300 sites with co-located soil microbial, plant, bird, and pollinator surveys along with climate and soil physicochemical information. Soil groups are analysed using metabarcoding of the 16S, ITS1 and 18S DNA regions, allowing in depth characterisation of microbial and soil animal biodiversity. C_LIO_LIWe explore biodiversity relationships along three aspects of community composition: First, we assess correlation between the alpha diversity of different groups. Second, we assess whether biotic turnover between sites is correlated across different groups. Finally, we investigate the co-occurrence of individual taxa across sites. In each analysis, we assess the contribution of linear or non-linear environmental effects. C_LIO_LIWe find that a positive correlation between alpha diversity of plants, soil bacteria, soil fungi, soil heterotrophic protists, bees and butterflies is in fact driven by complex non-linear responses to abiotic drivers. In contrast, environmental variation did not account for positive associations between the diversity of plants and both birds and AM fungi, suggesting a role for biotic interactions. C_LIO_LIBoth the diversity and taxon-level associations between the differing soil groups remained even after accounting for non-linear environmental gradients. Aboveground, spatial factors played larger roles in driving biotic communities, while linear environmental gradients were sufficient to explain many group- and taxon-level relationships. C_LIO_LISynthesis. Our results show how non-linear responses to environmental gradients drive many of the relationships between plant biodiversity and the biodiversity of above- and belowground biological communities. Our work shows how different aspects of biodiversity might respond non-linearly to changing environments and identifies cases where management-induced changes in one community could either influence other taxa or lead to loss of apparent biological associations. C_LI

ecology↗

bioRxiv2021

Identification and predictability of soil quality factors and indicators from conventional soil and vegetation classifications

Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter, Organic matter humification, Soluble nitrogen, Microbial biomass, Reduced nitrogen and Soil humification index. Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.

ecology↗

bioRxiv2019

Beyond taxonomic identification: integration of ecological responses to a soil bacterial 16S rRNA gene database.

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalising syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterise taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth "CS") to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from the survey metadata. Specifically, we modelled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on putative landscape scale pH-abundance responses. We identify that most of the soil OTUs examined exhibit predictable abundance responses across soil pH gradients, though with the exception of known acidophilic lineages, the pH optima of OTU relative abundance was variable and could not be generalised by broad taxonomy. This highlights the need for tools and databases to predict ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER), and as a DADA2 database for use in bioinformatics pipelines. The further development of advanced informatics infrastructures incorporating modelled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

microbiology↗